Machine Learning for Improving Timing Accuracy

Timing analysis belongs to the cornerstones of digital integrated circuit design. But with increasing technology complexity and lower power supply voltages, vigilance is needed to ensure that the timing paradigm continues to be sufficient for its purpose. State-Of-the-art timing models utilize current or voltage waveforms that are generated from circuit simulation, but they require large amounts of storage -especially when power supply, temperature, and process variations are included. Also, Miller coupling between the input and output of a gate, which can cause an up to a 70% error in propagation delay, is poorly modeled.In this paper, we propose a novel machine learning-based approach to waveform modeling in digital circuits. By leveraging machine learning techniques, we are able to improve the accuracy and predictive capabilities of timing models while simultaneously reducing model size by more than 5×.