Automation of input control of transistors using deep learning models and analysis of their latent space

Reliable automation of input control of transistors in microelectronics requires the detection of deviations in multichannel volt-ampere characteristics, however, threshold and simple statistical methods are poorly generalized to new and experimental structures, do not detect previously unseen defects and ignore nonlinear inter-channel dependencies. We offer a framework for detecting anomalies based on neural network models trained on the results of monitoring normal devices. Within the framework, we compare the family of autoencoders (MLP/LSTM/CAE) and specialized methods (USAD, GBAD), as well as explore the obtained latent spaces for interpreting the work of neural networks. The developed framework was verified on the basis of 14.2 thousand 180 nm VAC field-effect transistors with expert marking. The GBAD (F1 = 0.857) and regularized CAE (F1 = 0.857) models showed the best results in the test. The results set reproducible baselines for detecting anomalies in multichannel I–V transistor data and highlight the advantage of explicit modeling of the interchannel structure and latent representation analysis to enhance portability and interpretability.