A Non-invasive Technique to Detect Authentic/Counterfeit SRAM Chips

TL;DR

This paper introduces a non-invasive method to identify the manufacturer origin of SRAM chips, effectively detecting counterfeit chips to enhance security and reliability in critical systems.

Contribution

It presents a novel manufacturer attestation framework for SRAM chips that does not require exhaustive registration, improving counterfeit detection accuracy.

Findings

F1 score of 93% for manufacturer identification

F1 score of 71% for part-number identification

Validated on 345 SRAM chips from major manufacturers

Abstract

Many commercially available memory chips are fabricated worldwide in untrusted facilities. Therefore, a counterfeit memory chip can easily enter into the supply chain in different formats. Deploying these counterfeit memory chips into an electronic system can severely affect security and reliability domains because of their sub-standard quality, poor performance, and shorter lifespan. Therefore, a proper solution is required to identify counterfeit memory chips before deploying them in mission-, safety-, and security-critical systems. However, a single solution to prevent counterfeiting is challenging due to the diversity of counterfeit types, sources, and refinement techniques. Besides, the chips can pass initial testing and still fail while being used in the system. Furthermore, existing solutions focus on detecting a single counterfeit type (e.g., detecting recycled memory chips). This work proposes a framework that detects major counterfeit static random-access memory (SRAM) types by attesting/identifying the origin of the manufacturer. The proposed technique generates a single signature for a manufacturer and does not require any exhaustive registration/authentication process. We validate our proposed technique using 345 SRAM chips produced by major manufacturers. The silicon results show that the test scores ($F_{1}$ score) of our proposed technique of identifying memory manufacturer and part-number are 93% and 71%, respectively.