Machine Learning-Based Parametric Fault Detection in Analog and Mixed-Signal Circuits
Jules Kouamo, Emmanuel Simeu, Michele Portolan · 2025
The quest for reliable testing of analogue and mixed-signal circuits remains a cornerstone in the advancement of integrated circuit technology, driving innovation in high-stakes areas such as aerospace and automotive systems. The continuous nature of the signals and the variability of the components make the task both time consuming and difficult to automate. This work presents a general methodology to detect parametric faults in analogue circuits using machine learning models combined with Monte Carlo simulations, advanced signal transformations and optimal classifier selection. The proposed framework is adaptable to different circuit architectures, as demonstrated by simulations of RLC bandpass filters and Sallen-Key filters. In particular, our approach incorporates new metrics that balance classification accuracy with implementation complexity and computational efficiency, providing a scalable solution adapted for automation and integration into an embedded test environment.