Analog functional safety with anomaly detection

Abstract

In some examples, systems and methods may be used to improve functional safety of analog or mixed-signal circuits, and, more specifically, to anomaly detection to help predict failures for mitigating catastrophic results of circuit failures. An example may include using a machine learning model trained to identify point anomalies, contextual or conditional anomalies, or collective anomalies in a set of time-series data collected from in-field detectors of the circuit. The machine learning models may be trained with data that has only normal data or has some anomalous data included in the data set. In an example, the data may include functional or design-for-feature (DFx) signal data received from an in-field detector on an analog component. A functional safety action may be triggered based on analysis of the functional or DFx signal data.