Adaptive Data Augmentation Based on Ensemble Learning for Indirect Test of Analog Circuits

The non-parametric indirect test offers a promising strategy to reduce the testing cost of analog integrated circuits. However, as a data-driven method, its performance is constrained by imbalanced and limited training data. To address this problem, we propose an ensemble-based adaptive data augmentation method (E-ADA). The E-ADA reshapes the original dataset toward a more uniform distribution by generating synthetic samples. Through perturbation, the number of generated samples is adapted to local data density. Multiple augmented datasets are then used to train diverse machine learning models targeting the same specification, each with a unique training set. During inference, all sub-models are executed in parallel, and their outputs are averaged to yield the final estimation. This strategy improves stability across sparse and dense data intervals by correcting distribution imbalance. Meanwhile, the proposed approach enhances robustness of estimation via ensemble averaging. Moreover, diversity among sub-models is enhanced by diverse datasets, further enhancing stability. Experimental results on simulation data and commercial ICs demonstrate that E-ADA improves both estimation stability and accuracy.