Unsupervised Learning-based Early Anomaly Detection in AMS Circuits of Automotive SoCs

With the proliferation of safety-critical applications in the automotive domain, it is imperative to guarantee the functional safety of circuits and components constituting automotive systems, e.g., the electrical and/or electronic subsystems in automotive vehicles. Analog and Mixed-Signal (AMS) circuits, prevalent in such systems, are more susceptible to faults than their digital counterparts, due to advanced manufacturing nodes, parametric perturbations, environmental stress, etc. However, their continuous signal characteristics provide an opportunity for early anomaly detection, which in turn, facilitates the deployment of safety mechanisms to prevent eventual system failure. Towards this end, we propose a novel unsupervised machine learning-based framework to perform early anomaly detection in AMS circuits. Our approach involves anomaly injection in various circuit locations and individual components to develop a training dataset encompassing a wide range of possible anomalous scenarios, feature extraction from observation signals, and clustering algorithms to facilitate anomaly detection. To this end, we propose a novel centroid selection technique for the unsupervised learning algorithms, which is tailored for detecting anomalies in AMS circuits. This approach furnishes high fidelity anomaly detection by identifying the ideal cluster centers corresponding to anomalous and non-anomalous signals. Furthermore, time series-based analysis is proposed to improve and expedite the anomaly detection performance. We evaluated our solution using a case study of two AMS circuits commonly present in automotive systems-on-chips. Our experimental results exhibit that the proposed approach furnishes up to 100% accuracy. Additionally, the time series-based technique reduces the anomaly detection latency by 5×, thereby demonstrating the efficacy of our solution.