Digital Twin–Based Modeling and Health Management of Analog and Mixed-Signal Circuits

Abstract

Modern electronic systems are based on analog and mixed-signal (AMS) circuits that interface the physical world to digital number-crunching units. The changing process variations, aged effects, environmental stress and inability of observing them when operational is increasingly becoming a challenge to guarantee their reliability and long term performance. The paper proposes a modeling and health control framework of analog and mixed-signal circuits based on digital twins, which allows providing real-time monitoring, predicting the performance of the system, and diagnosing faults proactively. The proposed digital twin combines both physics-based circuit models and data-driven learning strategies to reproduce the dynamic behavior of AMS circuits with different operating conditions accurately. Through constant alignment of simulation models with real-time measurements, the digital twin records parametric drifts, nonlinearities, and degradation by-products like bias temperature variability and electromigration. Health indicators such as gain deviation, compensating drift and bandwidth reduction, and variation in noise are reported and processed to determine a circuit state and further life expectancy. The efficacy of the suggested method in early fault detection, performance degradation monitoring, and reliability improvement is proved by a case study with analog and mixed-signal representatives blocks. Both simulation and experimental findings demonstrate that the digital twin enhances prediction accuracy and provides an opportunity to make timely maintenance-related decisions as compared to traditional methods of working with steady models. The suggested framework presents a scalable and powerful approach to smart health management of new-generation electronic systems to uphold better reliability, less down time, and long service life.