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<p>The increasing reliance on third-party intellectual property (IP) cores, coupled with the globalization of the semi-conductor manufacturing process, has raised significant security concerns in the design and deployment of modern integrated circuits (ICs). One of the most insidious threats in this domain is the insertion of Hardware Trojans (HTs), malicious modifications, or additions to the design of a circuit that can compromise its functionality, leak sensitive information, or render it vulnerable to external attacks. Creating efficient test patterns capable of identifying these anomalies, particularly in the midst of noise and the vast design space of contemporary integrated circuits, continues to be a significant challenge. This paper presents a novel hybrid approach that integrates particle swarm optimization (PSO) and reinforcement learning (RL) to generate optimized test patterns aimed at enhancing rare-gate activation and Trojan-trigger activation. Experimental results in ISCAS’85 and ’89, and ITC’99 benchmark circuits demonstrate that the proposed PSO-RL approach outperforms traditional heuristic methods, including MERO and TRIAGE, in terms of both average fitness and trigger coverage. Comparative analysis with standard PSO reveals that integrating RL yields improved trigger activation, particularly for complex circuits such as AES-128 from MIT-CEP and under hard-trigger conditions, thereby supporting its scalability.</p>

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Adaptive Test Pattern Generation for Hardware Trojan Detection using PSO and Reinforcement Learning

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Sandip Chakraborty,
  • Aniket Mandal,
  • Anindan Mondal,
  • Bibhash Sen

摘要

The increasing reliance on third-party intellectual property (IP) cores, coupled with the globalization of the semi-conductor manufacturing process, has raised significant security concerns in the design and deployment of modern integrated circuits (ICs). One of the most insidious threats in this domain is the insertion of Hardware Trojans (HTs), malicious modifications, or additions to the design of a circuit that can compromise its functionality, leak sensitive information, or render it vulnerable to external attacks. Creating efficient test patterns capable of identifying these anomalies, particularly in the midst of noise and the vast design space of contemporary integrated circuits, continues to be a significant challenge. This paper presents a novel hybrid approach that integrates particle swarm optimization (PSO) and reinforcement learning (RL) to generate optimized test patterns aimed at enhancing rare-gate activation and Trojan-trigger activation. Experimental results in ISCAS’85 and ’89, and ITC’99 benchmark circuits demonstrate that the proposed PSO-RL approach outperforms traditional heuristic methods, including MERO and TRIAGE, in terms of both average fitness and trigger coverage. Comparative analysis with standard PSO reveals that integrating RL yields improved trigger activation, particularly for complex circuits such as AES-128 from MIT-CEP and under hard-trigger conditions, thereby supporting its scalability.