Making, Maintaining, Supply Chain and Logistics

The Materials for Extreme Environments thrust is exploring new materials, innovative architectures, and development processes that will significantly enhance the performance and persistence of DoD platforms operating in extremely harsh environments. Materials with superior strength, functionality, and resiliency are critical for enabling DoD platforms, weapons and other components to operate and persist under conditions including, but not limited to, extremely high or low temperatures, turbulence, ionizing radiation, and/or corrosive environments. Recent developments in materials such as high entropy alloys, superconducting materials, and infiltrated carbon fiber composites hold promise for achieving material solutions for improved survivability in a wide range of harsh environmental conditions. Similarly, advancements in material design, processing and manufacturing are enabling novel material architectures that can further enhance performance and resilience in structures such as leading edges, windows and apertures, propulsion systems, and space structures. Exemplar areas of research within the Materials for Extreme Environments thrust include the following: 1) high temperature materials for hypersonic platforms, 2) high temperature window and aperture materials, 3) radiation and/or electromagnetic pulse (EMP) hardened electronics for space platforms, 4) coatings for platform survivability in corrosive environments, 5) active and passive cooling methods for apertures and forward-facing vehicle features, and 6) superconducting and magnetic materials for novel propulsion systems. Prior to FY 2026, this program was funded in PE 0602715E, Project MBT-01.

The Quantum Technologies Studies and Concepts examines emerging technologies and system concepts that employ quantum technology or exploit quantum phenomena for computation, networking, or sensing. The feasibility of achieving potential improvements, in terms of resources, schedule, and technological risk, is also evaluated. The results from these studies and initiatives are used to formulate future programs.

The H6 program is developing the first tactical-grade clock. Tactical-grade clocks are ultra-small, low power, fieldable and can maintain the timing needed for DoD-relevant applications in challenging environments. Precise timing in a tactical package will decouple operations from GPS dependence, overcoming a significant operational vulnerability for the warfighter. Precise tactical-grade clocks from H6 will enable increased signal assurance and pervasive communications security in high-jamming regions. Additionally, H6 will enable real-time, physical monitoring and tracking of warfighters and special forces and will play a critical role in search and rescue through the ability to maintain precise time over a long mission duration without having to re-establish external communications. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The preponderance of evidence shows that commercial quantum computers will be a key economic driver in the 21st century. It is plausible that these machines will be constructed by commercial companies in the near future. An appropriate hedging strategy is needed to prevent strategic surprise from unanticipated commercial breakthroughs, ensure privileged access to disruptive capability, and maximize U.S. economic benefit. To quantify and reduce this risk, the Quantum Benchmarking initiative (QBI) program will execute an incremental investment plan that rigorously evaluates commercial activities, aggressively develops and tests key prototypes for activities that show credible potential and prepares for full-scale deployment when feasible. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02. The FY 2026 request for Quantum Benchmarking Initiative (QBI) includes $282,000 thousand of discretionary and $250,000 thousand of mandatory (reconciliation) for a total of $532,000 thousand. The mandatory funds will continue quantum computing evaluations and development of supporting technologies. Further information for this reconciliation request is provided in Section 20009 (INDOPACOM Capabilities) of the Reconciliation Exhibit.

Measurement and control of the electromagnetic spectrum is a key area of research for the Department of Defense (DoD). Spectrum dominance requires quick and efficient control of electromagnetic radiation from low frequencies to X-rays. In classical antenna theory, the sensitivity-bandwidth product is fundamentally limited by the physical shape and size of the antenna. This performance degrades significantly as the antenna becomes electrically small, that is, the physical size becomes much smaller than the electromagnetic wavelength of operation. The Macaroni program seeks to develop electrically-small receivers and transmitters with performance that exceeds the current state-of-the-art (SoA). Recent advances in quantum sensors, materials science, electromagnetic shielding, laser technology, resonators, cryogenic systems, and vacuum components have pushed the SoA in sensing technologies. For transmitters, new insights in active antenna technology, control schemes, methods of impedance matching, and strategies for volume filling present new opportunities. Furthermore, recent efforts in piezoelectrics, magnetoelectrics, high-index materials, and multiferroic materials may be leveraged. Prior to FY26, this program was funded in PE 0602716E, Project ELT-02.

The Robust Quantum Sensors (RoQS) program seeks to bring quantum sensors to DoD platforms. While quantum sensors have demonstrated exceptional laboratory performance in several modalities (magnetic and electric field, acceleration, rotation, and gravity, etc.), their performance degrades once the sensor is placed on moving platforms due to electric and magnetic fields, field gradients, and system vibrations. RoQS seeks to overcome these challenges through innovative physics approaches to quantum sensing. RoQS program aims to develop and demonstrate quantum sensors that inherently resist performance degradation from platform interferers and demonstrate them on a DoD platform. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-01.

The Quantum Augmented Network (QuANET) program is developing quantum-augmented networks that add novel security and covertness properties inherent in quantum communications to classical, non-quantum, network infrastructures that currently trade security against interoperability. Today, all digital communication paradigms use a network stack that consists of a layered set of software protocols. The higher layers are closer to applications on computers and servers, commonly called nodes, while the bottom layers are closer to the physical implementation, i.e., network cables. State-of-the-art networks commonly rely on security at the top layers of the stack, assuming this security also mitigates potential attacks on lower layers. Unfortunately, advanced persistent threat (APT) attacks are defeating many existing state-of-the-art capabilities and increasing cyber defense costs to U.S. commercial and government entities. The QuANET program seeks to augment existing software infrastructure and network protocols with quantum properties to mitigate attack vectors. QuANET will develop the hardware, protocols, and software tools required to operationalize quantum communications for sensitive missions and critical infrastructure and demonstrate these capabilities in an operational, quantum-augmented network. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

Efforts conducted under this thrust aim to study and refine emerging opportunities and concepts in artificial intelligence (AI) and human machine symbiosis. AI has tremendous implications for warfighting. In advance of kinetic conflict, AI can provide enhanced intelligence preparation of the battlespace and more timely indications and warning to prevent strategic surprise by the adversary. During kinetic conflict, AI can enable real-time situational awareness and dynamic replanning and force control to maximize success on the battlefield. While commercial-sector advances in AI have tremendous significance for national security, the Department of Defense (DoD) must actively address AI needs that the private sector cannot monetize and does not prioritize. A major focus for this thrust is trustworthy AI, which is AI that is reliable, accurate, explainable, and resilient to attack. Technical challenges include robustness of AI systems in novel, uncertain, unanticipated, and/or adversarial situations; efficiency and timeliness of AI development, test, evaluation, approval, and certification processes; assurance of agentic AI solutions for mission-critical applications; and AI architectures for low-resource environments and platforms. If successful, this thrust will significantly accelerate AI innovation in many national security domains and reduce the time and cost for the DoD to develop and deploy new AI-enabled capabilities.

The Artificial Intelligence Cyber Challenge (AIxCC) program is developing and demonstrating techniques for automated discovery and remediation of software vulnerabilities at speed and at scale to secure widely-used, critical code. Current automated vulnerability discovery and remediation tools are based on techniques such as fuzzing, logical reasoning, and genetic algorithms, but are limited in terms of effectiveness and user support. AIxCC leverages recent dramatic advances in artificial intelligence (AI) and machine learning, such as large pre-trained models and neurosymbolic AI, as the basis for new automated cyber security technologies and tools. AIxCC employs a contest model where teams use their automation and tooling to complete vulnerability discovery and remediation challenges. Performer teams are selected for the AIxCC competition based on their capability to leverage advances in AI to create usable, automated tools for vulnerability discovery and remediation and focus on tools suitable for broad deployment and applicable to critical infrastructure sectors. AIxCC competitors will train and develop their systems to find and fix vulnerabilities in widely used open-source software, focusing on software used in critical infrastructure. Each competitor system is evaluated on real-world critical infrastructure software suites and is scored based on their results in terms of absolute performance and performance relative to other competitor systems. Winning teams will receive cash awards. If successful, AIxCC will create novel AI-enabled cyber vulnerability remediation technology and tools for securing code at the scale and speed needed to defend U.S. critical infrastructure from cyber-attacks. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Scientific Feasibility (SciFy) program is developing computational methods to measure the feasibility of claims to enable accurate assessments of scientific content. Automated scientific content generation, via rapidly improving large pre-trained models, has the potential to disrupt the U.S. technology base in times of crisis and to distort the global race for technological dominance in key areas. Similarly, false capability claims can have significant negative implications for national security and international relations. To address these threats, SciFy will focus on methods for assessing the scientific feasibility of claims by using automated reasoning to decompose claims into constituent, verifiable parts. Assessing each component will involve referencing existing technological advancements, foundational scientific principles, data, software, models, simulation results, and industry standards or benchmarks. SciFy will create methods that go beyond automated fact-checking by addressing complex component interactions and operational constraints as well as evaluating logical consistency, system integration, and compatibility considerations. If successful, SciFy will enable the U.S. to reliably determine whether claimed scientific and technological capabilities are practical and realistic when considered as a whole, even when theoretically possible in parts. This applied research funding supports applied research efforts that are part of the larger basic research SciFy activity under PE 0601122E, Project EMR-01. Prior to FY 2026, this program was funded in PE 0601101E, Project CCS-02 and PE 0602303E, Project IT-04.

The Assured Neuro Symbolic Learning and Reasoning (ANSR) program is developing new hybrid artificial intelligence (AI) algorithms that deeply integrate symbolic reasoning with data driven learning to create trustworthy AI-based systems. For purposes of this program, an AI-based system is considered trustworthy if it is: (a) robust to domain informed and adversarial perturbations; (b) supported by an assurance framework that creates and analyzes heterogenous evidence towards safety and risk assessments; and (c) predictable with respect to some specification and model of fitness. ANSR develops hybrid AI algorithms for which it is possible to develop evidence-based techniques that support confident assurance judgments. The key idea is to interleave symbolic and neural representations in hybrid AI algorithms that can acquire symbolic knowledge through learning and perform symbolic reasoning at scale to deliver robust inference, generalize to new situations, and provide evidence for assurance and trust. ANSR technologies will be demonstrated and evaluated on Department of Defense (DoD) use cases, such as autonomy, where trustworthiness is essential. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Open Price Exploration for National security (OPEN) program aims to increase supply chain resilience and enable more efficient critical mineral markets by leveraging advances in artificial intelligence (AI) prediction and forecasting to increase structural price, supply, and demand transparency. OPEN will construct structural price predictions from fundamental and observable critical mineral input costs and increase the accuracy and precision of supply and demand forecasts by leveraging this structural price in conjunction with advances in AI and economic modeling. Today, critical mineral markets and supply chains are vulnerable. International supply shocks can lead to large and rapid critical mineral price spikes with immediate economic ramifications, and commodities purchase transactions (e.g., offtake agreements) are negotiated leveraging a mix of opaque and flawed pricing data. OPEN will leverage a decomposition of a critical mineral price into four components (input costs, supply/demand shocks, distortions due to noncompetitive behavior, and stochastic fluctuation) to construct transparent estimations of an approximate marginal cost for critical minerals indexed by time and geographic location, and will estimate supply and demand forecasts for critical minerals that take into account geopolitical factors, energy fluctuations, and technological innovations in recycling and supply chain management. Technology developed under this program will transition to applicable government entities and commercial partners. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Learning Introspective Control (LINC) program is developing machine introspection and learning technologies to characterize a modified or damaged military platform from its behavior and update the control law to maintain stability and control. The current approach to handling platform modification or damage places the burden of recovery and control on the operator, whether the operator is human or an autonomous controller. In contrast, a platform equipped with LINC technology would continually compare the real-time behavior of the platform as measured by on-board sensors with a learned model, determine if the current observed behavior of the platform differs from that model in ways that might compromise stability and control, and implement an updated control law when required. The LINC capability would aid operators in maintaining effective control of military platforms that suffer damage or have been modified in the field to address emergent requirements identified during operations. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Anticipatory and Adaptive Anti-money Laundering (A3ML) program aims to develop graph search-based algorithms that enable anticipation of and adaptation to money laundering tactics, techniques, and procedures (TTPs) in order to prevent money laundering in the global financial system. Today, anti-money laundering (AML) practitioners accumulate evidence of money laundering largely via manual searches of myriad commercial and financial intelligence (FININT) databases and rule-based AML systems that lack flexibility to adapt to changing TTPs. Because of the static nature of current AML processes and the limited capabilities of AML tools, novel money laundering schemes can go on for years. To address this challenge, A3ML will develop and apply graph search and subgraph isomorphism techniques that reference high-level models of money laundering to extract specific money laundering TTPs from heterogeneous transaction data and to anticipate and adapt to new money laundering TTPs in a timely fashion. If successful, A3ML technologies and tools would enhance AML practices across U.S. Government (USG) and the private sector. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Artificial Intelligence Quantified (AIQ) program is developing technologies for assessing the capabilities of generative artificial intelligence (genAI) to enable mathematical guarantees on performance. Current state-of-the-art methods for genAI quantification and assessment are ad hoc, deal with only the simplest of capabilities, and are not properly grounded in a rigorous theory. The AIQ approach combines mathematical methods with advances in measurement and modeling to enable rigorous quantification of genAI capabilities. The program will address three capability levels: 1) the specific problem level, which addresses quantification for a given problem; 2) the classes of problem level, which address quantification of a given problem class; and 3) the natural class level which addresses the reverse question of the class of problems quantifiable for a given genAI architecture. If successful, AIQ will provide guarantees on the performance of genAI technologies. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) program aims to advance understanding of the operational vulnerabilities of the military use of artificial intelligence (AI) enabled systems in battlefield environments and enhance the robustness of AI-enabled systems against real-world adversarial attacks. AI-enabled systems have been shown to be vulnerable to adversarial AI attacks, but most attacks studied to date are not physically realizable or assume the adversary already has some level of system access. SABER anticipates adversaries will develop combined AI-cyber-physical attacks and formalize these attacks in tactics, techniques, and procedures (TTPs). To address such capable AI adversaries, SABER will evaluate AI-enabled military systems in realistic operational settings to quantify the risks of deploying AI-enabled systems on the battlefield. Simultaneously, SABER will develop technologies and TTPs for continuous monitoring, integration, and deployment of counter-AI techniques to enhance the robustness of AI-enabled systems. SABER technologies will enable the formation of Department of Defense (DoD) AI red teams that continuously integrate and employ emerging counter-AI techniques and tools, establishing a sustainable model for operational AI red teaming. If successful, SABER will empower military AI red teams to confidently assess and secure US AI-enabled battlefield systems against attack by sophisticated adversaries. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT) program is developing techniques to robustly transfer learned autonomy from fast abstract simulations to autonomous platforms in real-world environments. The autonomy levels of unmanned systems of today are limited because the modeling and simulation (M&S) training environments do not account for the data domain shift common when translating M&S outcomes to the real world, a phenomenon sometimes referred to as the sim2real gap. The TIAMAT approach integrates symbolic structures with neural structures to transfer learned autonomy more realistically and robustly. TIAMAT will enable the use of fast abstract simulations by anchoring the learning and transfer of autonomy on semantically consistent components shared across simulations and real environments, so-called "semantic anchors". For TIAMAT, semantically consistent components of particular importance include militarily relevant phenomena that remain consistent in the source and target environments, such as mission objectives, special instructions, subject matter expert guidance, rules of engagement, and the laws of physics. Autonomy transfer using TIAMAT methods and techniques will reduce the complexity of the autonomy learning and transfer problems to the comparatively simpler points of reference in the anchored representation. If successful, TIAMAT transfer of M&S-based learning will enable more rapid and robust training and deployment of autonomous systems at higher levels of autonomy. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

The Kallisti program seeks to develop new capabilities in theory of mind for behaviors in independent agents. Actions taken by independent agents are determined by their belief about the state of their environment, the risks and rewards associated with these beliefs, and the strategy they use to react to the risks or rewards they believe exist in their environment. This program will develop an algorithmic theory of mind that enables behaviors in independent agents by inferring and understanding the belief states that will deter or compel distributions of actions in an independent agent. Motivated by the Theory of Mind demonstrated in the Strategic Chaos Engine for Planning, Tactics, Experimentation and Resiliency (SCEPTER) program (budgeted in PE 0603760E, Project CCC-02), Kallisti will further develop this theory of mind to allow projection of an agent's future behavior based on the agent's perceived strategy, enabling influence over the agent. The program seeks not only to understand an agent's current strategy but also to find a decomposed version of the strategy into relevant basis vectors to track strategy changes under non-stationary assumptions. The primary goal is to utilize the algorithmic theory of mind to deter unwanted actions and compel more favorable actions in an agent's action space. Technology developed under this program will transition to the Services. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

Building on insights developed from the Open Price Exploration for National security (OPEN) program (budgeted in PE 0602303E, Project IT-04), the National Security Economic Theory (NASCENT) program will perpetuate a policy and market feedback loop between the United States Government (USG) and private-sector executives to support more effective geoeconomic actions with precisely defined positive national security outcomes. Current approaches to geoeconomics are based largely on qualitative ideas, drawn primarily from international relations and legal backgrounds, and are often non-falsifiable with limited scope. Furthermore, the lack of a body that facilitates neutral and open discussion and iterative development of geoeconomic theory has hindered innovation and renders the development of theory and tools of economic warfare cost ineffective. NASCENT will overcome these challenges by creating a membership-based forum where the USG, academia, and the private sector can collaboratively and iteratively design and rigorously evaluate geoeconomic mechanisms. NASCENT will combine techniques from economic mechanism design and experimental economics to work backwards from precisely described national security outcomes, elicited from across the USG, to understand what economic incentives and mechanisms enable those outcomes. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-04.

Efforts conducted under this thrust aim to create and sustain cyber security technologies to secure the defense and civilian digital ecosystems. There is strong interest in technologies to enable tech refresh of legacy software systems using domain-specific and memory-safe languages. Mathematically based software development techniques, commonly referred to as formal methods, will be created to enable the development and sustainment of provably secure software for civilian and military information systems, cyber-physical and embedded systems, critical infrastructure, and other computation-intensive, mission-critical systems. Artificial intelligence and machine learning will be developed and applied to enhance cyber security and achieve greater operational resilience through cyber monitors and agents that can detect and characterize cyber threats, engage cyber adversaries, prioritize operationally important workflows, maintain essential services, and complete critical missions.

The Reclaiming Bus-based Systems During Compromise (Red-C) program will develop and demonstrate novel approaches for resilient, self-healing, bus-based systems. The embedded computing systems common to modern vehicles and other cyber-physical systems typically use a bus-based architecture for communication among the various sensor, processor, actuator, and controller components organic to the platform. Due to the physical isolation of embedded computing systems, most standard buses currently in use were developed under an implicit assumption that all bus-connected components are secure and can be trusted. As a consequence, most bus-based systems are highly vulnerable in that compromise of any component on the bus can quickly spread to other bus-connected components. Red-C aims to address this critical vulnerability and create resilient, self-healing, bus-based systems through advances to instrumentation and response. For instrumentation, Red-C aims to enhance the individual components on the bus to enable them to function as sensors that collectively monitor the bus and peer bus components to detect a cyber-attack. Then, having identified that an attack is underway, Red-C-enhanced bus peers will collaborate on software patch creation to remediate bus vulnerabilities and enable recovery of bus functionality. Red-C will implement these bus resilience techniques as firmware updates that can be deployed to existing bus-based systems and components. If successful, Red-C technologies will mitigate bus vulnerabilities in military and civilian platforms and cyber-physical systems. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03.

The Hardening Development Toolchains Against Emergent Execution Engines (HARDEN) program is developing techniques and tools to anticipate, isolate, and mitigate emergent system behaviors and thereby improve security of complex integrated software. Today's software development toolchains and testing methodologies provide very limited means for reasoning about adversarial reuse of code as written and designed. This limitation results in unwitting creation of stable, reliable patterns of emergent behaviors within systems that adversaries can reuse in attacks. The HARDEN approach to preventing adversarial code reuse is to create techniques, tools, metadata, and instrumentation for reasoning about emergent execution at all stages of the software development life cycle (SDLC) and to flag code segments and design patterns where there is high potential for adversarial reuse and emergent execution. The utility of HARDEN technologies will be assessed by applying them to integrated software systems and critical system elements, such as bootloaders. If successful, the technologies developed by HARDEN will facilitate efficient mitigation of complex code-reuse and emergent-execution vulnerabilities at early SDLC stages and provide the stronger roots-of-trust required by zero trust architectures and high assurance integrated military software systems. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03.

The Business Process Logic (BPL) program is developing techniques to characterize and resolve vulnerabilities in business logic systems to protect and assure defense-critical workflows for government and business. Automated workflows written in business logic (BL) control much of the world's enterprises, from administration and operation of seaports to the assembly of weapons systems. Losses due to BL faults and vulnerabilities can range from annoyances to business-threatening outcomes. Therefore, it is important to identify and correct potentially problematic logic issues such as one-way actions or lost resources as early as possible. The BPL program is developing tools to extract workflow representations from BL and use those representations to identify, characterize, and mitigate faults and vulnerabilities in BL scripts and templates automatically. The technologies developed by BPL will enable increased assurance for manufacturing and assembly and greater efficiency for logistics and supply chain management. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03.

The Intelligent Generation of Tools for Security (INGOTS) program is developing techniques to identify, triage, and assess exploitability of chainable vulnerabilities. Today, sophisticated cyber attacks link multiple vulnerabilities together into exploit chains that bypass software and hardware security measures to compromise critical, high-value systems. Accurately understanding risk is critical for both developers and defenders within cyberspace, but the metrics currently in use do not account for the multiple factors that differentiate an innocuous software flaw from a chainable vulnerability. INGOTS is developing semi-automated tools and techniques to characterize and measure the interdependent exploitability of vulnerabilities and a new vulnerability severity metrology that characterizes and measures interdependent exploitability. With the INGOTS vulnerability measurement capability, developers and defenders will improve software and hardware resiliency by rapidly identifying and prioritizing their most dangerous flaws. The INGOTS program is also funded in PE 0602025E, Project MSL-02. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03 and PE 0602716E, Project ELT-02.

The Cyber Agents for Security Testing and Learning Environments (CASTLE) program is developing an artificial intelligence (AI) toolkit to instantiate realistic network environments and train AI cyber agents to enable resilient network operations against advanced persistent threats (APTs). CASTLE formulates network hardening as a reinforcement learning (RL) problem and teaches RL agents to operate through the post-breach behavior of widely available penetration testing tools. Over progressive rounds of attack and defense, agents explore defensive actions to proactively stop ongoing attacks while maintaining operationally relevant workflows. Environments execute agents inside instrumented subnets that are deployed to live networks and simulate defensive actions that counter APT tools. Agent execution produces calibrated datasets for progressively improving simulations. The defensive AI cyber agents developed under CASTLE will provide the Department of Defense with continual security assessments of critical networks and real-time response to cyber attacks. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03.

The Provably Weird Network Deployment and Detection (PWND²) program is developing formal models of emergent communication pathways to fundamentally improve how we deploy and detect resilient hidden networks in the real world. Authoritarian regimes are increasingly able to monitor and target internet communications, leaving many populations unable to communicate freely and safely. In response, the internet freedom and national security communities manually design hidden communication systems that use existing/available infrastructure and then validate these systems via empirical testing and analysis. These tests have difficulty capturing the range of real-world network environments and require infeasible amounts of testing to assess comprehensively at scale. PWND² is developing principled approaches for creating hidden communications systems with formal guarantees and for defending against use of these techniques by an adversary. If successful, PWND² technologies would enable the rapid and automatic generation and deployment of hidden communications systems with high assurance and strong guarantees. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03.

The Pipelined Reasoning of Verifiers Enabling Robust Systems (PROVERS) program is creating scalable mathematically based technologies, tools, and practices to achieve continuous reasoning about complex systems that can support software development pipelines. These mathematically based techniques, or formal methods, enable rigorous modeling, reasoning, and proving of diverse properties of software code or design models, such as the absence of a specific type of defect or security vulnerability. PROVERS integrates formal methods into a modern incremental and iterative development process by running tools at each code commit and delivering results to developers when they can most effectively remediate discovered issues. PROVERS focuses on creating and sustaining a body of evidence that can co-evolve with the system under change to support continuous assessment and ensure that the system remains free of identified categories of defects and security vulnerabilities through its lifetime. Key PROVERS objectives include enabling proof maintenance and repair capabilities at a cost that is proportionate to code change; integration of formal methods with code, properties, and proofs in a single workflow that reduces human involvement; providing improved explanations to facilitate proof repair; and automating formal methods-based software analysis to support software developers that are not formal methods experts. PROVERS technologies will facilitate the agile development and continuous improvement of mission-critical software systems that meet the high security and quality standards required by the Department of Defense (DoD). Basic research for this program is funded in PE 0601101E, Project CCS-02. Prior to FY 2026, this program was funded in PE 0602303E, Project IT-03.

The Constellation program is comprised of multiple projects that are developing technologies, capabilities, and prototype systems to enable full spectrum military cyberspace operations to defend the U.S. and deter, disrupt, and defeat adversary cyber actors. Technologies of interest include, but are not limited to, artificial intelligence, machine learning, and data science; resilient software, networking, and computing systems; formal methods and program analysis; data and information assurance; and cyber threat intelligence. High relevance is achieved through close coordination with U.S. cyber operators and the use of development, security, and operations (DevSecOps) and other collaborative development processes. High velocity is achieved through streamlined acquisition, assessment, approval, and deployment processes. Constellation development and deployment pipelines enable the rapid and continuous delivery of cyber technologies, capabilities, and prototype systems into operational use for the Department of Defense (DoD). The Constellation program is also funded in PE 0603469E, AET-03 to facilitate rapid transition of cyber technologies and laboratory prototypes from applied research to operational prototypes. Prior to FY 2026, this program was funded in PE 0603760E, Project CCC-05, and PE 0602303E, Project IT-03.

The Electronic Technology Studies and Concepts thrust is developing the technologies that can enable a new generation of integrated circuits. Technologies such as advanced organic-electronic hybrid circuits, new photonic / optical / quantum components, and novel concepts in electronics and Micro-Electro-Mechanical Systems (MEMS) will be developed to address applications that are beyond the reach of traditional electronics. The feasibility and impact of the magnitude of the improvements these non-traditional circuits can achieve will also be evaluated.

While the technical sophistication of radar systems has advanced tremendously since their inception, specifically with the use of digital circuitry that enables fine-tuned control over both waveform generation and received signal processing, the basic principles upon which radar operates have not varied greatly. Some properties of the classical electromagnetic (EM) field have not yet been exploited fully, whether due to past limitations in sensor capabilities or lack of insight into the possible benefits of leveraging the more subtle aspects of the EM field. One of these underutilized properties is the fact that the EM field is a spatiotemporally varying three-dimensional (magnetic or electric) vector field, meaning it has a tensorial nature. The Emon program will investigate the potential utility of constructing radar systems that benefit more completely from the tensorial nature of the EM field. We are particularly interested in looking at waveforms with spatially varying phase fronts. Interrogating an environment with a signal that offers more degrees of freedom in which interaction phenomena can be encoded should enable us to extract not only more information, but more salient information. Radar sensing would then become even more useful than it is currently, providing enhanced insight into target characteristics beyond position and radial velocity. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-01.

The Scalable Analog Neural networks (ScAN) program will increase neural network (NN) inferencing capabilities at the edge and simultaneously reduce the size, weight, and power (SWaP) needed to support inferencing on edge platforms. Currently, sensor outputs are digitized at the edge and then transmitted to the command center for processing. ScAN aims to skip the digitization step and instead perform analog inferencing onboard the edge platform by operating directly on the analog sensor outputs. ScAN objectives are to enable 2000-fold reduction in the power needed for inferencing. ScAN technology will enable intelligence generation at the edge for missions that collect large amounts of sensor data, such as hyper-spectral imaging for unmanned aerial systems. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-01.

The Highly Accelerated Learning of Vibratory Systems (HALOVS) portfolio investigates the foundational limits and transformative potential of vibratory sensors for positioning and navigation in GPS-denied environments. By leveraging advances in 3D micromachining, material science, and dynamic modeling, HALOVS explores novel architectures and principles through efforts examining ultrafast microsystems, higher-order composite resonators for extra resilience, and chemistries and monolayers for anti-aging kinematics. These efforts aim to address critical challenges in sensor performance, including operating beyond linear regimes to achieve high-velocity motion, leveraging nonlinearities for enhanced resilience to shock and vibration, and extending sensor longevity by addressing drift and aging at the molecular level. The insights and technologies developed within HALOVS promise to redefine the design space and practical limits of vibratory sensors, delivering capabilities essential to national security and global positioning system (GPS)-denied missions. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-01.

The Microsystem Induced CAtalysis (MICA) program will develop advanced concepts for microsystem control of biological function. The program will seek hardware demonstrations of molecular catalysts immobilized to microsystem surfaces so that catalyst activity is controlled by physical forces generated by the microsystem. Additionally, the program will seek high-accuracy modeling and simulation of such integrated molecular microsystems. Through these demonstrations, MICA aims to answer three critical questions: (1) how can microsystems be used to actively control molecules? (2) what are the different microsystem physics that can be used to drive catalyst function? and (3) what co-design approaches can be used to integrate the different physics of microsystems and molecules? Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-01.

The Disruptive Microtechnologies Studies and Concepts will develop technologies to ensure a sustainable domestic supply chain of advanced microsystems. This includes the design, assembly, fabrication, and testing technologies for advanced microsystems that exploits and extends beyond commercial activities. It takes advantage of innovations in photonics, optics, materials, energy, biology, and advanced three dimensional heterogeneous integration (3DHI) for the highest performance technology. In doing so, the goal is to revolutionize domestic industry and enable safe and reliable access to disruptive technology.

The Scalable On-Array Processing (SOAP) program is designed to achieve scalable algorithms and processing architectures to overcome the inherent digital bottlenecks that severely limit today's wideband operation on arbitrarily large elemental digital phased arrays. SOAP aims to reduce the computational complexity of array processing as a function of element count, from exponential to linear scaling. SOAP also seeks to move the processing from physically separated back-end processors to processors integrated into the array, in order to fully process all the information generated at the element level, with no elemental information loss. To achieve these aims, SOAP will design processors that can be distributed within the array, as close to the elements as possible. These processors should be connected and networked in such a way that the data from any element can be processed by any processor. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The ELectronics for G-band ARrays (ELGAR) program is developing the integration technologies needed to create compact, high-performance G-band (220 GHz) array front-end electronics to enable phased array antenna systems for DoD communications and sensing. ELGAR will address the key technical challenges that prevent III-V electronics from realizing high-performance G-band arrays, namely achieving efficient, compact G-band III-V monolithic microwave/millimeter wave integrated circuit power amplifiers (MMIC PAs) with high output power density and achieving low loss off-chip interconnects between adjacent G-band array components. In particular, ELGAR will develop III-V compatible, silicon-like fabrication and integration approaches to enable compact, high-power density, high efficiency G-band MMICs and arrays. The technologies developed will support applications including high data rate communications in size, weight, and power-constrained platforms. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The COmpact Front-end Filters at the ElEment-level (COFFEE) program is developing and demonstrating compact, high frequency radio frequency (RF) filter technology without compromising performance, specifically low insertion loss and high-power handling. The new filtering technology will enable interference rejection capability, efficient spectral management, and coexistence with commercial 5G applications. It is projected that COFFEE filter technology will enhance the resilience of military microwave and mm-wave radar and communication systems for DoD spectral dominance into the future. For commercial applications, COFFEE will result in more efficient use of mm-wave frequency allocations for 5G networks. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Intensity-Squeezed Photonic Integration for Revolutionary Detectors (INSPIRED) program is developing compact, ultra-low-noise optical detectors. Low-noise detection is vital to all optical science and technology, but the quantum nature of light imposes a fundamental quantum limit on a conventional optical detector's noise performance. Recent experiments have demonstrated that exotic quantum states called squeezed light can be harnessed to overcome the quantum limit, albeit from bench-scale apparatuses that ultimately restrict the application of squeezed-light-enhanced detectors to esoteric applications such as gravitational-wave astronomy. The INSPIRED program is leveraging recent advances in chip-scale quantum optics and materials to realize optical detector modules operating well below the quantum noise limit in form factors that enable deployment in applications such as biosensing, navigation, and communications. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The M-STUDIO program will realize a universal defect-free heterogeneous integration methodology, informed by emerging nano-scale material growth with surface free-energy driven defect termination and non-thermal-equilibrium synthesis, to achieve defect-free multi-layer heterogeneous materials with nanometer dimensions and atomically sharp interfaces. Specifically, M-STUDIO program will achieve: (1) Material synthesis with one heterogeneous interface: a semiconductor layer on lattice mismatch substrate with a total thickness 10 nm (> 100x reduction from the state-of-the-art (SOA)) and with 103/cm2 defect density, and (2) demonstrate the synthesis scalability with multiple heterogeneous interfaces: multiple 10 nm heterogenous semiconductor layers on lattice mismatch substrate with atomically sharp transition. In addition, M-STUDIO integration technology is compatible with leading edge and future advanced node semiconductor manufacturing process. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The PROtein SEquencing (PROSE) program seeks to bring protein sequencing capabilities to relevant missions. A protein's sequence largely determines its structure and ultimately, its function. PROSE will demonstrate molecular readers that can accurately read a broad array range of amino acids and post translational modifications (i.e., letters) in sequence for unknown protein samples. The program will seek hardware demonstrations of novel read element designs integrated with microsystem architectures. These approaches must overcome the challenges associated with reading long protein sequences with high accuracy at high throughput. Additionally, given the scale of the current alphabet, and scaling estimates of future alphabet size protein letter space, approaches must also demonstrate the ability to read a relevant alphabet size. This alphabet size is chosen to provide confidence that PROSE technologies can scale, with future commercial development, to read the entire protein alphabet.

The Intelligent Generation of Tools for Security (INGOTS) program is developing techniques to identify, triage, and assess exploitability of chainable vulnerabilities within widely used secure computing platforms. Today, sophisticated cyber attacks link multiple vulnerabilities together into exploit chains that bypass software and hardware security measures to compromise critical, high-value systems. Accurately understanding risk is critical for both developers and defenders within cyberspace, but the metrics currently in use do not account for the multiple factors that differentiate an innocuous software flaw from a chainable vulnerability. INGOTS is developing semi-automated tools and techniques to characterize and measure the interdependent exploitability of vulnerabilities and a new vulnerability severity metrology that characterizes and measures interdependent exploitability. With the INGOTS vulnerability measurement capability, developers and defenders will improve software and hardware resiliency by rapidly identifying and prioritizing the most dangerous flaws. The INGOTS program is also funded in PE 0602025E, Project MSL-04. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02 and PE 0602303E, Project IT-03.

Efficient, high-speed scientific computing architectures are a ubiquitous requirement for applications including modeling of complex physical systems, advanced device designs, and multiscale computations of dynamical phenomena such as climate models or turbulence. Current state-of-the-art computing systems requires prohibitive amounts of energy and time to perform such calculations. The NanoWatt Platforms for Sensing, Analysis, and Computation (NaPSAC) program aims to develop a novel computational architecture for massively parallel, ultralow power "in-memory" computation. NaPSAC-based computing architectures can potentially yield transformative impact by enabling beyond-state-of-the-art computational speed and accuracy. Applications of immediate relevance to the DoD include simulations of turbulent flows, multiscale electromagnetic simulations of plasma dynamics, advanced semiconductor device design, and the modeling of high-performance materials. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

Many commercial, industrial, and defense systems experience thermal environments that are beyond the performance and survivability of today's physical sensors. The High Operational Temperature Sensors (HOTS) program is developing a high-performance sensor platform for operation at extreme high temperature. The program will be validated by demonstration of a dynamic pressure sensor achieving 1 MHz bandwidth and 90 dB dynamic range with survivability at 800°C for one hour. The mission motivator for HOTS is hypersonic vehicle development but HOTS technology will find application in combustion engine research, superhot rock geothermal energy development, and petrochemical processing. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Machine Learning and Optimization-guided Compilers for Heterogeneous Architectures (MOCHA) program will develop a new generation of compiler technology that uses machine learning to automatically generate and optimize compilers that realize the full potential of heterogenous architectures. The ending of Dennard scaling, which has limited the increase of clock speeds of digital circuits, has led to computing architectures that include a variety of co-processors and accelerators that enable high performance in specific domains. Traditional compilers, which are the software that turns source code into machine instructions, do a poor job of optimizing code for heterogenous ensembles of processing units and accelerators, and a portion of the potential benefit of accelerators is lost. Extending compilers to handle heterogeneity is at present a manual task that is both time consuming and error prone, limiting the ability to upgrade mission-critical systems in a timely manner. MOCHA will apply machine learning techniques to the problems of compiler adaptation and extension by semi-automatically generating the main components of compilers for heterogeneous architectures. The use of machine learning will reduce human effort and development time while improving the quality of the machine code emitted by the compiler in terms of measures, such as performance, memory size, and power consumption. If successful, MOCHA will speed production of the compilers needed to capitalize on emerging specialized processor hardware in heterogeneous computing architectures for high performance military systems. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Space Power Conversion Electronics (SPCE) program seeks to develop highly-efficient, radiation-tolerant point of load (POL) converters for low-earth-orbit satellites. In today's space power systems, power transistors for POL converters have derated operating voltage to maintain radiation tolerance, resulting in decreased POL efficiency and limiting the satellite's available power, capabilities, and battery lifetime. To address this deficiency, SPCE will develop high-performance, radiation-tolerant high voltage switches and compact passives by exploiting advanced wide-bandgap semiconductor advanced material synthesis, novel device architectures, and advanced manufacturing processes. High efficiency and compact radiation tolerant POL will be realized with advanced gate drive/control circuitries and novel 3D heterogeneous integration technology. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

3D heterogeneously integrated (3DHI) microelectronics will be a key driver of the next wave in electronics performance. However, the nation's current single-event effect (SEE) radiation testing infrastructure lacks the ability to analyze and qualify emerging 3D devices for operation in high radiation environments. To fill this gap, the Advanced Sources for Single-event Effect Radiation Testing (ASSERT) program is developing new source technologies to create charge tracks with deep penetration depths for SEE qualification of 3DHI topologies and packaging, providing the means to selectively probe device topologies to inform engineering design, and generate data to validate developing models and codes and to provide training sets for optimization. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Quantum Inspired Classical Computing (QuICC) program will implement quantum-inspired algorithms using classical dynamic systems in novel computing architectures for the efficient solving of complex optimization problems. Currently, too much computational energy is required to solve mission-scale optimization problems leading to sub-optimal solutions and excessive computation times. This program will create frameworks for analyzing the computational advantage provided by quantum-inspired algorithms and perform the hardware and algorithm co-design needed to reduce the required energy to optimally solve mission-scale problems. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Heterogeneous Adaptively Produced Photonic Interfaces (HAPPI) program aims to revolutionize information transmission within microsystems by achieving a 1000x increase in connectivity density through advanced photonic solutions. The program focuses on developing three-dimensional optical routing capabilities both within and between chips, enabling unprecedented levels of information movement and processing throughout integrated systems. This includes the potential to interface with both electronics and other optical elements, enhancing the flexibility and reach of the system. HAPPI's advancements will enable large-scale photonic circuits with thousands of components unlocking new microsystem architectures for applications such as signal processing, free-space communications, remote sensing, digital computing, and atomic sensing. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Additive Manufacturing of MicrosystEms (AMME) program will revolutionize microsystem manufacturing by leveraging selective material synthesis and 3D patterning to enable a new class of microsystems. Additive Manufacturing (AM) has enabled complex single-material geometries that were previously impossible to produce via traditional manufacturing methods. However, microsystem manufacturing has not exploited AM due to fundamental limits of material quality, resolution, and print throughput. The AMME program will use selective material synthesis to create high-quality material precursors that permit simultaneous printing of conductors and insulators with high-resolution and high-volume throughput. Additionally, AMME will focus on commercialization of this technology such that the Department of Defense and intelligence community can quickly adopt the productized system to fabricate novel microsystems. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

Minitherms3D is developing thermal management solutions for the three-dimensional heterogeneous integration (3DHI) of microelectronics to accelerate the growth of compact, high-performance microsystems. 3DHI microsystems are enabling technologies for phased array systems and dense computing for artificial intelligence and machine learning applications. Minitherms3D will reduce the size, weight and power (SWaP) of high-performance 3DHI microsystems by developing novel methods to transfer heat from within the 3D stack to its boundaries, transmit it to a remote location, and reject it to ambient air. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Optimum Processing Technology Inside Memory Arrays (OPTIMA) program aims to realize a high throughput, compact, energy-efficient, and adaptable compute-in-memory (CIM) accelerator that is compatible with very large-scale integration (VLSI) fabrication. Traditional accelerators based on von Neumann architecture have limitations in terms of computational power efficiency and speed. By demonstrating a CIM accelerator with an array of Multiply Accumulate Macros (MAMs) consisting of a large number of Multiply Compute Elements (MCE) to perform the matrix multiply operating within memory elements, the power and speed challenges can be overcome. The program goal is to showcase high-performance CIM accelerator with innovative signal processing circuitry and architectures, with a focus on optimizing both area and power efficiency. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Technologies for Heat Removal in Electronics At the Device Scale (THREADS) program is developing technologies to overcome transistor thermal limits to realize robust, high power density transistors that operate near their fundamental electronic limit of radio-frequency (RF) output power. DoD's RF transmitters increasingly use high-power gallium nitride (GaN) wide bandgap (WBG) transistors, which provide a 5X improvement in RF power output compared to the legacy gallium arsenide (GaAs) technology. Achieving high RF power output while maintaining a transistor operating temperature below the nominal maximum reliable operation temperature faces two challenges. The first challenge is reducing thermal resistance within the device. This will be achieved by leveraging recent advances in epitaxial growth processes and phonon bridges to reduce semiconductor material thermal resistance. The second challenge is more efficiently moving heat away from the transistor hot spots. This will be achieved through novel transistor topologies and by leveraging recent advances in the integration of 2D and 3D cooling structures and high thermal conductivity materials, such as diamond, into the transistor. THREADS will demonstrate high efficiency X-band transistors and power amplifier (PA) test vehicles with an output power density of 16X higher than production GaN amplifiers. THREADS technology will enable increased range for radar, communications, and electronic warfare systems. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Digital RF Battlespace Emulator (DRBE) program is developing a large-scale, interactive, emulated radio frequency (RF) environment, providing the DoD with the capability to cost-effectively evaluate adaptive, intelligent, and spatially distributed next-generation RF systems. DRBE is leveraging advances in massively multi-core computing hardware and high-bandwidth digital cross-connects to emulate realistic RF environments accounting for RF platform movement, signal propagation effects and delays, signal interference, and interactions between RF systems. An electronics architecture supporting the power and latency requirements demanded by these emulation environments does not currently exist. DRBE is pursuing three technical thrust areas: architecture, massively multi-core computing, and scenario modeling. The resulting test environment will allow plug-and-play connections for hundreds of RF systems in a battlespace test. Multi-system exercises will then be quickly executed through many different combat scenarios and variations. DRBE is serving to develop concept of operations (CONOPS), inform battle plans, and fine-tune the performance of both individual and large groups of RF systems. Additional development started in 2024 greatly expands the input/output bandwidth of DRBE to support for much larger RF scenarios. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

Advanced infrared (IR) detectors and thermal imaging systems underpin a vast DoD application space including biochemical detection; infrared Search-and-Track; and terrestrial and space-based Intelligence, Surveillance, and Reconnaissance. Current IR detectors suffer from numerous limitations including poor sensitivity, poor signal bandwidth, or the need for expensive cryogenic cooling. The Optomechanical Thermal Imaging (OpTIm) program will develop a new modality of low size, weight, and power, room temperature IR detectors capable of quantum-level sensitivity, thereby enabling transformative enhancements to DoD capabilities including, but not limited to, night vision, surveillance, multispectral detection, and remote detection of trace industrial pollutants and greenhouse gases. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Ultra-Wide BandGap Semiconductors (UWBGS) program will develop and optimize ultra-wide bandgap (UWBG) materials and fabrication processes required to enable the next revolution in semiconductor electronics. UWBGS will establish the foundation for the creation of producible and reliable, high performance UWBG devices for a variety of DoD (and commercial) applications. These include but are not limited to: high power radio frequency (RF) switches; high power density RF amplifiers; high RF power protection device; high voltage switches for power electronics; high temperature electronics and deep ultraviolet light-emitting diodes and lasers. The program will address the key technical challenges that are limiting the performance of UWBG device. These challenges include realizing high quality UWBG materials, ability to tailor electrical characteristics of UWBG materials; ability to create homo- and heterostructures with abrupt junctions and low defect density; and the realization of ultra-low resistance electrical contacts. UWBGS will fabricate device test structures to quantify the improvements in these areas. To be successful, the program will leverage recent advances in UWBG materials. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

Next Generation Microelectronics Manufacturing (NGMM) creates new software design tools to enable the development of novel three-dimensional heterogeneous integration (3DHI) microsystems and associated test vehicles with the NGMM program. This program addresses deficiencies in the capabilities and flexibility of current electronic design automation (EDA) workflows and simulation tools by establishing and refining the NGMM EDA capability, 3D assembly design kit (3D-ADK), and designs for devices and test vehicles. The design tools developed will be validated through design challenges. These design challenges provide the opportunity to explore approaches that will improve and accelerate the adoption of 3DHI standardized chip-to-chip interfaces and package optimization. Leading-edge chip designs will be fabricated, and subsequently integrated into 3DHI designs in multi-project demonstration runs. Additional research related to this effort is funded within PE 0603739E, Project MT-16. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

The Faithful Integration Reverse-engineering and Emulation (FIRE) program will develop tools to find and patch vulnerabilities within cyber-physical systems. A cyber-physical system operates in the physical world using hardware sensors to perceive the analog environment, digital software for processing, and actuators to interact with the environment. Cyber-physical vulnerabilities arise from the composition of hardware, software, and physical components where each component may not be vulnerable in-and-of itself. FIRE will develop novel modeling and simulation techniques to help expedite finding and patching vulnerabilities in cyber-physical systems. Prior to FY 2026, this program was funded in PE 0602716E, Project ELT-02.

Efforts in the Disruptive Sensing and Analysis thrust examine and develop opportunities to increase our understanding of adversarial systems and through improved processing techniques, models, and signals of opportunity that will generate low-cost, actionable solutions for enhanced detection and characterization of events and systems of interest. Focus areas include improved sensing and assessment. Prior to FY 2026, this program was funded as Awareness in Materials Processing Technology in PE 0602715E, Project MBT-01.

The Complex Modeling Studies and Analysis thrust is exploring novel approaches to modeling and controlling physical environments in various conditions for improved platform design. Nonlinear flow conditions represent some of the oldest unsolved challenges in physics. The ability to model high-Reynolds number classical turbulence, for instance, or turbulent cascades in compressible fluids, is extremely limited. Focus areas include new modeling and simulation tools to understand complex physical conditions, and to aid engineers in design, regardless of scale. Prior to FY 2026, this program was funded as Access in Materials Processing Technology in Technology in PE 0602715E, Project MBT-01.

The Enabling Production Studies and Concepts thrust is developing technologies that enable the production of molecules, materials, and parts in an expeditionary setting that will untether military forces from supply chains and enable a continuous global presence. Focus areas include making products at the point of need from local feedstock, developing the ability to use non-optimized materials in manufacturing; accelerating part qualification; and new approaches to developing room temperature superconductors and efficient thermoelectric materials. Prior to FY 2026, this program was funded as Making and Maintaining in Materials Processing Technologies in PE 0602715E, Project MBT-01.

The Reconfigurable Systems thrust is developing new approaches to enable more rapid and robust adaptation of defense systems and systems-of-systems to changing mission requirements and unpredictable environments. This includes development of capabilities across sensing, perception, planning and control for autonomous, high-speed operation in cluttered environments without Global Positioning System (GPS) information. This also includes development of capabilities to manipulate and control adversary sensory perception and/or situational awareness. Additional work in this thrust focuses on how sensing systems and military systems-of-systems are designed for real-time resilient response to dynamic, unexpected signals and contingencies. Research is developing a more unified view of system behavior that allows better understanding and exploitation of complex interactions among components, including development of formal mathematical approaches to complex adaptive system composition and design. These capabilities will impact autonomous systems and systems-of-systems, including those that involve humans, in a variety of DoD-relevant contexts. Prior to FY 2026, this program was funded in PE 0602715E, Project MBT-01.

Research in the Chemical Processing for Force Protection thrust is focused on the development of new chemical approaches and technologies across a broad spectrum of DoD needs. One area involves development of innovative approaches for scalable small molecule synthesis coupled with predictive tools for route design, possibly offering a new strategy to discover how to make new molecules such as pharmaceuticals and explosives. A second area includes qualification of new molecules made using agile manufacturing platforms. A third focus leverages advances in automation to develop safe, reproducible experimental approaches for systematic development of energetic materials. Another focus is to develop the tools and processing methods to safely turn locally available materials in a given location into reliable, point/time-of-need energetics, for battlefield requirements. In addition, investments in this thrust will advance chemical characterization, information management and analysis, and automation. Prior to FY 2026, this program was funded in PE 0602715E, Project MBT-01.

The Grainy Day program will develop innovative approaches and new methods to address challenges in passive accurate placement of Unattended Ground Sensors (UGS) from Unmanned Aerial Systems (UAS) and military aircraft. The range of initial deployment altitudes will require compensation for jet streams, trade winds, and low-level weather in real time, and the UGS devices will be ruggedized to withstand extreme environments in flight as well as ground impact while maintaining functionality. Because of the large number of devices delivered, scalability and manufacturability will be established via rapid fabrication and commercial chip foundries. Program success will bring the Department of Defense (DoD) a new affordable, scalable, and resilient capability to accurately place UGS devices with flexible mission sets deployed from a distance. Technology developed under this program will be transitioned to the Services and other government partners.

Previously part of MBT-01, Program Element 0602715E, the Burn n' Go program will increase the production rate and availability of solid rocket motors (SRMs) via development of novel materials and methods for tailorable, post-manufacturing thrust control. Complementary to efforts to improve supply chain resilience being developed under the Open Price Exploration for National security (OPEN) program funded in this PE, Project MSL-05, Burn n' Go will enable a paradigm shift in solid rocket motor production by creating a multi-mission standardized motor design for use across multiple existing and future weapons systems. Utilizing a standard design for SRMs in set diameter classes, Burn n' Go will enable suppliers to limit line switching times, improve tooling, accelerate quality control, reduce machining costs, and enable a more stable procurement stream, which can be flexed across Services and weapons. Burn n' Go will validate the developed technology through a series of component-level feasibility tests and integrated system-level static fire tests. Technology developed under this program will be transitioned to the Services and commercial Defense partners. Prior to FY 2026, this program was funded in PE 0602715E, Project MBT-01.

The Functional Materials and Devices thrust is developing advanced materials, components and systems to improve device performance for DoD structural, sensing, imaging and communication applications. One focus of this thrust involves development of advanced transductional materials that convert one form of energy to another for DoD-relevant applications. While promising transduction materials are known for a variety of applications, integration into devices has not been realized. Another focus area is the development of physics-based models that predict material behavior when illuminated by high peak power electromagnetic interference. A third focus area involves development of new multi-functional materials and device designs that will radically decrease the size, weight and power requirements of high-energy photon sources. Such devices should enable fieldable detection units for sensing, non-destructive evaluation of parts, and detection of DoD-relevant targets. A fourth focus area is developing new liquid-based, large-aperture imaging systems such as telescopes. Such telescopes would break the unfavorable, exponential scaling between aperture size and cost for normal telescopes and enable low-cost imaging platforms for ground- and space-based applications. Another focus area under this thrust involves novel nano-architected materials to enhance device-relevant properties for applications to quantum-enhanced sensors. Finally, novel design optimization approaches will be explored where material composition and microstructure are included as explicit, continuous variables alongside shape optimization. This co-optimization of shape and material together will enable new combinations of structural performance and sustainability for a variety of DoD applications. Prior to FY 2026, this program was funded in PE 0602715E, Project MBT-01.