Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning

arXiv:2404.01632v1 Announce Type: new
Abstract: Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and other factors, in comparison to their digital counterparts. However, their continuous signal characteristics present an opportunity for early anomaly detection, enabling the implementation of safety …

abstract analog applications arxiv automotive circuits components cs.lg cs.sy eess.sy functional machine machine learning mixed safety safety-critical signal systems through type unsupervised unsupervised machine learning vulnerable