DAWN: Efficient Trojan Detection in Analog Circuits Using Circuit Watermarking and Neural Twins
Jayeeta Chaudhuri, Mayukh Bhattacharya, Krishnendu Chakrabarty · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2024
As the globalization of integrated circuits (ICs) continues to advance, the threat of hardware Trojans has emerged as a major concern in ensuring the security and reliability of analog circuits. While a considerable body of prior work has focused on detecting digital Trojans in digital circuits, the detection of analog Trojans in analog circuits has received significantly less attention. We present DAWN, a sensitivity analysis-based analog Trojan detection framework using neural networks to identify potential analog Trojan hotspots and prevent them from being exploited through unauthorized modifications. We incorporate circuit watermarks in these hotspots to provide an additional layer of security. With these watermarks, any malicious modification to the circuit is automatically detected with high accuracy. We target the detection of stealthy, large-delay Trojans that might be inserted either during the chip design or fabrication stages. Experimental results for analog benchmark circuits and two commonly studied analog Trojans demonstrate the effectiveness of the proposed framework.