Automatic Surrogate Model Generation and Debugging of Analog/Mixed-Signal Designs Via Collaborative Stimulus Generation and Machine Learning

Abstract

In top-down analog and mixed-signal design, a key problem is to ensure that the netlist or physical design does not contain unanticipated behaviors. Mismatches between netlist level circuit descriptions and high level behavioral models need to be captured at all stages of the design process for accuracy of system level simulation as well as fast convergence of the design. To support the above, we present a guided test generation algorithm that explores the input stimulus space and generates new stimuli which are likely to excite differences between the model and its netlist description. Subsequently, a recurrent neural network (RNN) based learning model is used to learn divergent model and netlist behaviors and absorb them into the model to minimize these differences. The process is repeated iteratively and in each iteration, a Bayesian optimization algorithm is used to find optimal RNN hyperparameters to maximize behavior learning. The result is a circuit-accurate behavioral model that is also much faster to simulate than a circuit simulator. In addition, another sub-goal is to perform design bug diagnosis to track the source of observed behavioral anomalies down to individual modules or small levels of circuit detail. An optimization-based diagnosis approach using Volterra learning kernels that is easily integrated into circuit simulators is proposed. Results on representative circuits are presented.