On Multivariate Electrical Performance Machine Learning Driven Pre-Silicon IC Adaptive Verification

Abstract

This paper presents an adaptive pre-silicon integrated circuit verification algorithm that incorporates a machine learning algorithm. This approach can overcome the traditional corner-based process-voltage-temperature verification limitations considering variables coverage. The proposed solution employs an adaptive algorithm whose parameters have been analyzed in detail and optimized to achieve high performance. The optimal configurations for these parameters were demonstrated through the accuracy and efficiency of the algorithm. Additionally, this paper presents three methods that expand the use of the univariate algorithm to multivariate adaptive scenarios. These techniques aim to achieve the most accurate identification of worst-case circuit behavior through simultaneously modeling multiple electrical parameters (EP) outputs. The effectiveness of the proposed methods was validated through extensive testing on a large and diverse set of synthetic test functions that intend to replicate the behavior of real circuits. The algorithms consistency and accuracy are also validated on real Low Dropout Voltage Regulator (LDO) circuits.

Keywords

• IC pre-silicon verification
• Gaussian process regression
• machine learning