MMM: Machine Learning-Based Macro-Modeling for Linear Analog ICs and ADC/DACs

Performance modeling is a key bottleneck for analog design automation. Although machine learning-based models have advanced the state-of-the-art, they have so far suffered from huge data preparation cost, very limited reusability, and inadequate accuracy for large circuits. We introduce ML-based macro-modeling techniques to mitigate these problems for linear analog ICs and ADC/DACs. The modeling techniques are based on macro-models, which can be assembled to evaluate circuit system performance, and more appealingly can be reused across different circuit topologies. On representative testcases, our method achieves more than
$1700\times $
speedup for data preparation and remarkably smaller model errors compared to recent ML approaches. It also attains
$3600\times $
acceleration over SPICE simulation with very small errors and reduces data preparation time for an ADC design from 40 days to 9.6 h.