A Hybrid Machine Learning and Numeric Optimization Approach to Analog Circuit Deobfuscation
Dipali Jain, Guangwei Zhao, Rajesh Kumar Datta, Kaveh Shamsi · 2025
Oracle-guided circuit deobfuscation (or learning) is the problem of disambiguating an obfuscated (partially hidden) circuit given black-box access to it. This has applications in various hardware security areas such as analyzing the security of circuit obfuscation defense schemes, side-channel analysis, reverse engineering, and hardware Trojan detection. Generic deobfuscation of analog circuits has received less attention than the digital counterpart with existing methods relying on manual expert work to extract closed-form equations from the circuit. In this work, we move towards a significantly more automated process by using a combination of machine learning and Newton-method-based analog circuit optimization. We showcase how this hybrid scheme is superior to either standalone approach in terms of runtime and accuracy on a set of analog circuits that include amplifiers, filters, and oscillators. We achieve >98% average accuracy without any manual expert equation extraction in addition to demonstrating a superior resilience to process variation.