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Various studies have shown the advantages of using Machine Learning (ML)techniques for analog and digital IC design automation and optimization. Datascarcity is still an issue for electronic designs, while training highlyaccurate ML models. This work proposes generating and evaluating artificialdata using generative adversarial networks (GANs) for circuit data to aid andimprove the accuracy of ML models trained with a small training data set. Thetraining data is obtained by various simulations in the Cadence Virtuoso,HSPICE, and Microcap design environment with TSMC 180nm and 22nm CMOStechnology nodes. The artificial data is generated and tested for anappropriate set of analog and digital circuits. The experimental results showthat the proposed artificial data generation significantly improves ML modelsand reduces the percentage error by more than 50\% of the original percentageerror, which were previously trained with insufficient data. Furthermore, thisresearch aims to contribute to the extensive application of AI/ML in the fieldof VLSI design and technology by relieving the training dataavailability-related challenges.

Qualitative Data Augmentation for Performance Prediction in VLSI circuits