Abstract
Inverse analysis of semiconductor devices often suffers from non-unique solutions, known as multivaluedness, within a large-scale parameter space. Here, we show that a tandem neural network (tandem NN), which couples a pretrained forward model to an inverse model and jointly minimizes prediction and reconstruction losses, overcomes this challenge for amorphous In–Ga–Zn–O thin-film transistors. Trained on 1000 simulated transfer curves covering six intrinsic material parameters varied across ranges three to five orders of magnitude wider than in previous studies, the network infers multiple physical parameters from a single current–voltage curve in less than 1 ms with R2 = 0.99. The inferred parameters reproduce experimental current–voltage characteristics of lab-fabricated devices without additional fitting, confirming physical validity. Compared with conventional TCAD iterative fitting, the tandem NN provides significant acceleration and paves the way for autonomous experimentation for materials discovery, digital-twin frameworks in next-generation transistor manufacturing, and other multivalued inverse problem domains.