Machine-Learning-Based Error Detection and Design Optimization in Signal Integrity Applications

Evaluating the robustness of integrated circuits (ICs) against noise and disturbances is of crucial importance in signal integrity (SI) applications. In this paper, the addressed challenge is to build a software-based framework allowing for automated detection of failures and fast simulation-based evaluation of designs. In particular, these tasks are here addressed using anomaly detection (AD), a branch of machine learning (ML) techniques focused on identifying erroneous or deviant data. In the proposed framework, the ML model only requires the time-domain waveforms and no additional knowledge about the circuit nor about the errors to be identified. Specifically, a two-step approach to detect anomalous behaviors in output waveforms of digital ICs is proposed, comprising a first phase where the ML models are trained to learn relevant features describing the data and a second one where those features are used to identify anomalies with unsupervised or semisupervised AD techniques. Two relevant application examples validate the performance and flexibility of the proposed method.