2026-06-17 東京科学大学
◆研究チームは、物性値からデバイス特性を予測する順方向AIと、特性から物性値を推定する逆方向AIを直列接続したタンデム型ニューラルネットワークを開発し、逆方向AIが求めた物性値で元の特性を再現できるかを学習に組み込んだ。その結果、従来研究の約1000倍広いパラメータ範囲において、トランジスタ特性曲線1本から6種類の半導体パラメータを1ミリ秒未満で推定し、決定係数R²>0.99の高精度を達成した。従来法と比べて100万倍以上の高速化を実現し、半導体製造のリアルタイム品質管理や自律実験システムへの応用が期待される。
図1. 逆問題の概念図
＜関連情報＞
- https://www.isct.ac.jp/ja/news/2l3p7p9hum25
- https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.70437
タンデムニューラルネットワークによる多値逆問題の高速解決：酸化物半導体特性評価への応用 Tandem Neural Network Rapidly Solves Multivalued Inverse Problems: Application to Oxide-Semiconductor Characterization
Masatoshi Kimura, Keisuke Ide, Kuan-Ju Zhou, Atsushi Shimizu, Takayoshi Katase, Hidenori Hiramatsu, Kei Terayama, Hideo Hosono, Toshio Kamiya
Advanced Intelligent Systems Published: 27 May 2026
DOI:https://doi.org/10.1002/aisy.70437
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.
