CAMO: Counterfeit Attestation MOdule for Electronics Supply Chain Tracking and Provenance

供应链全球化和电子商务导致假冒电子产品贩运大幅增加，大流行病引起的微芯片短缺只会加剧这种贩运。当市场上没有高需求的电子产品时，它们就成为造假者的主要目标。除了知识产权（IP）侵权造成的数十亿美元损失外，假冒电子产品通常质量不合格，从而给医疗保健、食品和农业、通信、交通、能源、制造、应急服务、国防等关键部门的系统和基础设施带来风险。最近的立法，如《CHIPS法案》，强调需要制定打击假冒的程序，但关键利益相关者多年来一直拒绝采用必要的技术。可以通过物理检查、电气测试或防伪设计（DfAC）来检测和避免伪造。前两种一直是主流，但不太可靠，更昂贵，而且缺乏自动化。尽管DfAC原语只能包含在全新的电子产品中，但它们在准确性、身份验证成本和可伸缩性方面更有前景。此外，包括区块链在内的集中式和分散式分类账技术正在发展，以支持DfAC原始验证以及不可变的可追溯性和电子产品的来源。该项目旨在解决DfAC原始集成到微芯片和系统中的主要障碍。假冒认证模块（简称camo）将被开发出来，以有效地收集印刷电路板（PCB）上所有微芯片的DfAC输出，验证PCB本身没有被篡改，并保护结果与数字分类账的通信，以进行大规模认证。此外，还将探讨可方便地集成到普通微芯片模块中的轻量级DfAC原语。该项目可扩展、方便和廉价的假冒电子产品检测将为商业和国防工业节省成本，并将转嫁给消费者和纳税人。上述目标将通过四项任务来实现。首先，将探索一种称为谐振频率锁定（Res-FLO）的新型电路，用于pcb的自包含认证。Res-FLO捕获独特的，过程变化依赖于PCB配电网络的签名，以检测克隆和篡改PCB。接下来，为了支持芯片认证并在CAMO通信协议中提供安全性，将研究轻量级DfAC原语，以检测两种最常见的假冒芯片类型-回收和克隆。前者将通过测量模拟和数字低差稳压器（ldo）的退化来检测，而后者可以通过采用物理不可克隆功能（puf）来实现。第三，为PCB内部和分类账/区块链的通信提供保密性和完整性的廉价协议将由前两项任务的元素组成。也就是说，CAMO将利用大的输入/输出空间、机器学习（ML）抗攻击能力和强puf的多重测量来提高真伪分类的安全性和可信度，而不需要昂贵的加密和纠错方案。最后，CAMO原语和协议将用于概念验证，并捕获管理费用、老化和噪声的影响、对攻击的抵抗力等。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Supply chain globalization and e-commerce have caused a substantial rise in counterfeit electronics trafficking, which was only exacerbated by the pandemic-induced microchip shortage. When high-demand electronics are not available in the marketplace, they become prime targets for counterfeiters. Aside from billions of dollars lost from intellectual property (IP) infringement, counterfeit electronics are often of substandard quality, thereby creating risks for systems and infrastructure in critical sectors such as healthcare, food and agriculture, communications, transportation, energy, manufacturing, emergency services, defense, and more. Recent legislation, such as the CHIPS Act, stresses the need for procedures to combat counterfeiting, but key stakeholders have resisted adoption of the requisite technologies for years. Counterfeit detection and avoidance can be accomplished by physical inspection, electrical tests, or design-for-anti-counterfeit (DfAC). The former two have been the mainstay, but are less reliable, more expensive, and lack automation. Although DfAC primitives can only be included in brand new electronics, they are more promising with respect to accuracy, authentication cost, and scalability. Further, centralized and decentralized ledger technologies, including blockchains, are advancing to support DfAC primitive verification and for immutable traceability and provenance of electronics. This project aims to address the major barriers for DfAC primitive integration into microchips and systems. Counterfeit attestation modules (CAMOs for short) will be developed to efficiently collect DfAC outputs from all the microchips on a printed circuit board (PCB), verify that the PCB itself has not been tampered, and protect communication of results to a digital ledger for en masse authentication. In addition, lightweight DfAC primitives that can be conveniently incorporated into common microchip modules will also be explored. Scalable, convenient, and inexpensive detection of counterfeit electronics from this project shall result in savings for commercial and defense industries that will be passed on to consumers and taxpayers.The above goals will be achieved through four tasks. First, a novel circuit called Resonant Frequency Lock-On (Res-FLO) will be explored for self-contained authentication of PCBs. Res-FLO captures unique, process variation dependent signatures from PCB power distribution networks to detect cloned and tampered PCBs. Next, to support chip authentication and provide security in CAMO communication protocols, lightweight DfAC primitives will be investigated for detecting the two most prevalent counterfeit chip types – recycled and cloned. The former will be detected by measuring degradation in analog and digital low dropout regulators (LDOs) while the latter can be achieved by adopting physical unclonable functions (PUFs). Third, inexpensive protocols that provide confidentiality and integrity for communications within the PCB and to a ledger/blockchain will be composed from elements of the first two tasks. Namely, CAMO will exploit the large input/output space, machine learning (ML) attack resistance, and multiple measurements of strong PUFs to increase the security and confidence of authentic/counterfeit classification without the need for expensive cryptographic and error correction schemes. Finally, the CAMO primitives and protocols will be taped out for proof-of-concept and to capture overheads, impacts of aging and noise, resistance to attacks, etc.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.