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A deep-learning approach to realizing functionality in nanoelectronic devices


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.

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Fig. 1: Realizing functionality in a nanoelectronic device by using a DNN model.
Fig. 2: Sampling input–output data to train and test the DNN.
Fig. 3: DNN prediction of device functionality and verification.
Fig. 4: Prediction and verification of Boolean logic.
Fig. 5: Ring classification functionality.
Fig. 6: Feature mapping task.

Data availability

Data are available from the public repository at

Code availability

The custom computer code used here is available under the GNU General Public License v3.0 at


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We thank B. J. Geurts, U. Alegre Ibarra, B. de Wilde and L. J. Knoll for fruitful discussions. We are grateful to U. Alegre Ibarra for reading the manuscript carefully and providing useful input. We thank M. H. Siekman and J. G. M. Sanderink for technical support. We acknowledge financial support from the University of Twente, the Dutch Research Council (NWA Startimpuls grant no. 400-17-607) and the Natuurkunde Projectruimte (grant no. 680-91-114).

Author information




H.-C.R.E., M.N.B. and J.T.W. performed the measurements and the DNN modelling. B.v.d.V. and T.C. fabricated the samples. H.-C.R.E., M.N.B., J.T.W., P.A.B. and W.G.v.d.W. wrote the manuscript and all of the authors contributed to revisions. W.G.v.d.W. and H.-C.R.E. conceived the project and designed the experiments with input from M.N.B. and J.T.W. W.G.v.d.W., P.A.B., H.B. and H.-C.R.E. supervised the project.

Corresponding author

Correspondence to Wilfred G. van der Wiel.

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The authors declare no competing interests.

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Peer review information Nature Nanotechnology thanks Matthew Dale, Gunnar Tufte and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Sections 1–4 and Tables 1–10.

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Ruiz Euler, HC., Boon, M.N., Wildeboer, J.T. et al. A deep-learning approach to realizing functionality in nanoelectronic devices. Nat. Nanotechnol. 15, 992–998 (2020).

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