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Applications of Machine Learning and Machine Vision to Determine the Authenticity and Security of Microelectronics Parts in Weapons Systems

Description:

TECHNOLOGY AREA(S): Info Systemssensors, Electronics, Battlespace 

OBJECTIVE: Design and implement machine learning or machine vision technologies to determine the authenticity and security of microelectronics parts in weapon systems. These technologies may be purely software based or include a hardware component. 

DESCRIPTION: Determining the authenticity and security of microelectronics parts in weapons systems is of the upmost importance to the Department of Defense (DoD) [1,2]. Counterfeit and modified electronics pose a significant threat to the warfighter [1]. Although there has been much progress in detecting counterfeit parts [3], the sophistication of the counterfeits continue to evolve to evade detection. This evolution includes the introduction of cloned components [4] which are often undetectable through normal anti-counterfeit measures, including electrical testing. The Defense Microelectronics Activity (DMEA) is looking to apply machine learning and machine vision technologies as part of a detection or authentication scheme that will make avoiding detection impractical or even impossible. Such a scheme is only useful if implemented; therefore, it is essential for any proposed technology to be both cost and time efficient. There are many potential areas that these technologies can address, such as: validating golden units, classifying side channel signatures of microelectronics, automating general counterfeit detection, creating signatures for individual microelectronics, etc. The commercialization and technical evaluation for proposals will include the practicality of implementing the technology in the microelectronics supply chain. 

PHASE I: Conduct research on machine learning and machine vision technologies to determine the authenticity and security of microelectronic parts in weapon systems. These technologies may have an associated hardware component and may be part of broader system to secure the supply chain for weapon systems. The end product of Phase I is a feasibility study report, in which the following must be specified: 1) A clear description of the technology and how it is applied. 2) The computer hardware required to execute any required software program (e.g., workstation, cloud, GPUs, etc.). 3) Any associated hardware required as part of the technology solution (e.g. fixturing, sensors, cameras, tools, etc.). 4) A clear description of any required training sets or other requirements for the effective implementation of the technology. 5) A clear description of how to incorporate the technology into the process of developing, transporting, and inserting microelectronics into weapon systems to determine their authenticity and security. This description should include a business case for the time and cost to incorporate the technology. 

PHASE II: Develop a prototype of the Phase I concept and demonstrate its operation. Validate the performance in a way that realistically demonstrates how the technology would be deployed. This demonstration will include scalability of the technology in terms of capacity, cost, and time. 

PHASE III: There may be opportunities for further development of this innovation for use in a specific military or commercial application. During a Phase III program, the contractor may refine the performance of the design and produce pre-production quantities for evaluation by the Government. The proposed technology will be applicable to both commercial and government fields that require an added level of security for their microelectronics parts. Government applications include anti-counterfeit applications and acquisition processes for microelectronics parts for weapon systems and other critical systems. Commercial functions include the secure acquisition of microelectronics parts for critical applications such as medical, automotive, telecommunication, etc. 

REFERENCES: 

1: U.S. Senate Committee on Armed Services, "Inquiry into Counterfeit Electronic Parts in the Department Of Defense Supply Chain," May 2012. [Online]. Available: https://www.armed-services.senate.gov/download/inquiry-into-counterfeit-electronic-parts-in-the-department-of-defense-supply-chain.

2:  DARPA, "Supply Chain Hardware Integrity for Electronics Defense (SHIELD)," February 2019. [Online]. Available: https://www.darpa.mil/program/supply-chain-hardware-integrity-for-electronics-defense.

3:  SAE Aerospace Standard, "Test Methods Standard

4:  General Requirements, Suspect/Counterfeit, Electrical, Electronic, and Electromechanical Parts AS6171A," April 2018. [Online]. Available: https://www.sae.org/standards/content/as6171a/

5:  Government Accountability Office, "Counterfeit parts: DoD needs to improve reporting and oversight to reduce supply chain risk," GAO-16-236, Feb. 16, 2016. [Online]. http://www.gao.gov/assets/680/675227.pdf

KEYWORDS: Software, Machine Learning, Electronics, Anti-Counterfeit, Microelectronics, Machine Vision 

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