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3. Recovery of Intrinsic Heterojunction Bipolar Transistors Profiles by Neural Networks

Author

Caron, Grégoire, Juditsky, Anatoli, Guitard, Nicolas, Celi, Didier, ST Microélectronics, ST Microélectronic, Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), IEEE, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
[STAT]Statistics [stat], [SPI]Engineering Sciences [physics]
Abstract

International audience; Due to the shrinking size of devices, the calibration process for TCAD simulators is becoming increasingly complex with time mandatory to run simulations steadily increasing. In this work, we propose a Statistical Learning approach to predicting Figures of Merit (FoMs) of a Silicon Germanium Heterojunction bipolar transistor (HBT) 1D doping and germanium profile. The surrogate model thus built allows to simulate electrical outputs with high precision much faster than traditional simulations. This model is then used to solve the inverse design problem and recover profiles that match target FoMs.

Published
2022

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