O., Maheshwari, OmMaheshwari, OmO.D., Vyas, DevVyas, DevD.N.R., Mohapatra, Nihar RanjanMohapatra, Nihar RanjanN.R.2025-09-012025-09-0108186246550818621257978153863692397983503467879781479966585978076954348297807695488900818649909076952264597814673870021063966710.1109/VLSID60093.2024.000082-s2.0-85190388601https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190388601&doi=10.1109%2FVLSID60093.2024.00008&partnerID=40&md5=fa40a11ed9293c0b9a0daa8894122b5chttps://d8.irins.org/handle/IITG2025/29355In this work, we proposed a novel data-based methodology using artificial neural network (ANN) based classifier to predict current-voltage (I-V) characteristics of advanced FETs. The K-means clustering is employed to cluster and map the transistor drain current samples to centroids. This flexible and data dependent clustering enables accurate prediction over a wide parameter space for all regions of transistor operation. The classifier along with Savitzky-Golay filter predicts the I-V characteristics and the derivatives of I-V characteristics with an accuracy of 98%, outperforming the ANN regressor on a common test set. By utilizing the proposed model, an I-V characteristics can be predicted 8000 times faster as compared to an industry-standard TCAD tool. � 2024 Elsevier B.V., All rights reserved.EnglishClassification (of information)Current voltage characteristicsDrain currentForecastingK-means clusteringAccurate predictionClusteringsCurrent samplesCurrent-voltageCurrent-voltage characteristicsData dependentK-means++ clusteringNanosheet FETNetwork-basedParameter spacesNeural networksConference paper20240