AI and Blockchain for Secure Semiconductor Supply Chain Transparency

View at publisher
Authors: Bhavya Dhiman, Sudhakar Kumar, Sarjana Singh, Varsha Arya, Abhay Ratnaparkhi
Cited by: 4
References: 36

Abstract

Authentic, trusted semiconductor components are essential for safety-critical systems, but traditional manual, documentation-based verification cannot keep pace with complex, globalized chip supply chains and increasingly sophisticated counterfeit, recycled, and remarked ICs. AI-based technologies address this gap by detecting subtle counterfeit indicators and hidden patterns in supply chain data, focusing particularly on cases where conventional methods fail to flag suspicious parts. In this context, AI-encoded systems support semiconductor authenticity through three main capabilities: vision-based inspection for chip and packaging features, AI-generated data for origin and provenance verification, and AI-driven evaluation for smart trust scoring across suppliers and lots. These approaches enable automated chip inspection and anomaly detection in supply chain behavior, and their performance can be directly compared with traditional non-AI verification to demonstrate gains in accuracy, coverage, and reliability.

Fields of Study & Subjects

Physical Unclonable Functions (PUFs) and Hardware Security Big Data and Digital Economy Digital Media Forensic Detection

Publication details

Journal / source
Advances in computational intelligence and robotics book series
Publisher
IGI Global Scientific Publishing
Pages
249–286
Publication date
26 March 2026
Document type
Book chapter