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Peter Bobbert from the group M2N and the CCER is co-author of the publication A deep-learning approach to realizing functionality in nanoelectronic devices in Nature Nanotechnology of October 19, 2020. In this publication the complex electronic behavior of nanodevices consisting of dopant atoms in silicon contacted by electrodes is modeled with a Deep Neural Network (DNN). These devices can be used for energy-efficient reconfigurable logic. A desired logic functionality with specific settings of the electrode voltages is searched for within the DNN model, after which the functionality is realized by applying the found voltage settings to the physical device. This approach to realizing functionality is orders of magnitude faster than a direct search for functionality in the physical device. The approach can in principle be applied to finding functionality in any complex physical system. The work was carried out at the Center for Brain-Inspired Nano Systems (BRAINS) at the University of Twente, to which Peter Bobbert is attached as part-time professor.

Link: https://www.nature.com/articles/s41565-020-00779-y

Prof. Peter Bobbert (TU/e, CCER) is the co-author of the Nature paper Classification with a disordered dopant atom network in silicon  of Jan. 15, 2020.

It is our pleasure to invite you to the Applied Computational Science (ACOS) symposium that will take place on 30 October 2019 at the Dutch Institute for Fundamental Energy Research (DIFFER), located at the campus of the Eindhoven University of Technology (TU/e).

About CCER

The Center for Computational Energy Research  aims to explore pathways to future energy systems. This is done via computational simulations that complement experimental energy research. The center was founded in 2017 as the result of a joint initiative between the Department of Applied Physics of the Eindhoven University of Technology and NWO Institute DIFFER.

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