A deep learning approach to realize funtionality in nanoelectronic devices

Hans-Christian Ruiz Euler, Marcus N. Boon, Jochem. T. Wildeboer, Bram van de Ven, Tao Chen, Hajo Broersma, Peter A. Bobbert, Wilfred G. van der Wiel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

39 Citations (Scopus)
87 Downloads (Pure)


Many nanoscale devices require precise optimization to function. Tuning them to the desired operation regime becomes increasingly difficult and time-consuming when the number of terminals and couplings grows. Imperfections and device-to-device variations hinder optimization that uses physics-based models. Deep neural networks (DNNs) can model various complex physical phenomena but, so far, are mainly used as predictive tools. Here, we propose a generic deep-learning approach to efficiently optimize complex, multi-terminal nanoelectronic devices for desired functionality. We demonstrate our approach for realizing functionality in a disordered network of dopant atoms in silicon. We model the input–output characteristics of the device with a DNN, and subsequently optimize control parameters in the DNN model through gradient descent to realize various classification tasks. When the corresponding control settings are applied to the physical device, the resulting functionality is as predicted by the DNN model. We expect our approach to contribute to fast, in situ optimization of complex (quantum) nanoelectronic devices.
Original languageEnglish
Pages (from-to)992-998
JournalNature nanotechnology
Publication statusPublished - 19 Oct 2020


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