Patel, RutuRutuPatelMohapatra, Nihar R.Nihar R.MohapatraHegde, Ravi S.Ravi S.Hegde2025-08-312025-08-312023-01-0110.1016/j.sse.2022.1085052-s2.0-85141919341https://d8.irins.org/handle/IITG2025/25783Generation of training dataset for machine learning-based device design algorithm is expensive. To address this, we propose an active learning approach. Its efficiency is demonstrated through a Deep Neural Network (DNN) based Laterally Diffused Metal Oxide Semiconductor Field-effect Transistor (LDMOSFET) off-state breakdown voltage (BV<inf>DS,off</inf>) and specific on-resistance (R<inf>sp</inf>) predictor. Our results show the possibility of ∼50% reduction in the training dataset size without compromising the baseline accuracy. Specifically, we compared eight sampling techniques and found that Informative-Query by Committee (I-QBC) and Diverse Informative-Greedy Sampling (DI-GS) techniques work best with ∼1.87% Euclidean Norm of Prediction Error (ENPE).falseActive learning | Deep Neural Networks | LDMOSFET | Off-state breakdown voltage | Specific on resistanceArticleJanuary 20234108505arJournal4