Learn-by-Compare: Analog Performance Prediction using Contrastive Regression with Design Knowledge

This paper introduces Learn-by-Compare (LbC), a novel approach for analog performance modeling by employing semi-supervised contrastive regression. LbC employs a deep neural network encoder to come up with latent representations of sizing solutions by comparing similarity/dissimilarity of the underlying performance. Leveraging two levels of transistor level sizing data augmentation (DA), namely LS-DA and GS-DA, LbC produces new data samples by employing design knowledge. Experimental results highlight LbC's superior predictive accuracy compared to traditional regression methods. Offering a streamlined semi-supervised learning methodology, LbC effectively incorporates simple design knowledge and representation learning for efficient analog performance modeling.