Recent chip shortages and extended lead times have pushed system manufacturers to source semiconductors from secondary suppliers and distributors. While this helps maintain production, it also increases the risk of fraudulent or counterfeit Integrated Circuits (ICs) entering the supply chain. As semiconductor systems grow more complex and valuable, ensuring device authenticity and supply chain integrity has become a critical challenge.

proteanTecs deep data telemetry, enabled by embedded on-chip monitoring, introduces a new paradigm for supply chain security and counterfeit detection. By collecting rich parametric telemetry data directly from the silicon and analyzing it using advanced machine learning analytics, each device can be characterized through a unique operational fingerprint. This approach enables intrinsic device authentication and continuous monitoring of device health and behavior across the semiconductor lifecycle.

Unlike traditional methods that rely on external inspection or documentation, deep telemetry provides an internal, data-driven mechanism to verify device authenticity and detect anomalies that may indicate counterfeit, tampered, or unauthorized components.

This white paper presents a scalable framework for leveraging deep telemetry analytics to strengthen semiconductor supply chain integrity, enabling secure device authentication, validation, and ongoing monitoring from manufacturing through system deployment and operation.

What this paper explores:

• The rising risks of counterfeit and unauthorized ICs in today’s global semiconductor supply chains

• How deep telemetry data from embedded on-chip monitoring can create unique device fingerprints for authentication

• The role of machine learning analytics in identifying anomalies and detecting counterfeit components

• A scalable approach for continuous device verification and supply chain integrity across the full silicon lifecycle

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