Classification with a disordered dopant-atom network in silicon

• Published in: Nature (London) Vol. 577; no. 7790; pp. 341 - 345
• Main Authors: Chen, Tao, van Gelder, Jeroen, van de Ven, Bram, Amitonov, Sergey V., de Wilde, Bram, Ruiz Euler, Hans-Christian, Broersma, Hajo, Bobbert, Peter A., Zwanenburg, Floris A., van der Wiel, Wilfred G.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.01.2020; Nature Publishing Group
• Subjects: 142/126; 639/166/987; 639/301/357/995; 639/705/117; 639/766/25; 639/925/927/1007; Artificial evolution; Artificial neural networks; Atomic properties; Automatic classification; Boolean; Boolean algebra; Boron; Classification; CMOS; Computer applications; Computers; Data processing; Dopants; Energy efficiency; Evolution; Feature extraction; Filters; Handwriting; Humanities and Social Sciences; Learning algorithms; Logic circuits; Machine learning; Materials; Methods; multidisciplinary; Nanomaterials; Neural networks; Nonlinear systems; Nonlinearity; Parallel processing; Power; Room temperature; Science; Science (multidisciplinary); Semiconductor doping; Silicon; Temperature; Thermal cycling; Netherlands
• ISSN: 0028-0836; 1476-4687; 1476-4687
• DOI: 10.1038/s41586-019-1901-0

Classification is an important task at which both biological and artificial neural networks excel 1 , 2 . In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable 3 , 4 , simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density 5 , inherent parallelism and energy efficiency 6 , 7 . However, existing approaches either rely on the systems’ time dynamics, which requires sequential data processing and therefore hinders parallel computation 5 , 6 , 8 , or employ large materials systems that are difficult to scale up 7 . Here we use a parallel, nanoscale approach inspired by filters in the brain 1 and artificial neural networks 2 to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction 9 , 10 – 11 through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates 12 up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters 2 that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data 13 . Our results establish a paradigm of silicon-based electronics for small-footprint and energy-efficient computation 14 . The nonlinearity of hopping conduction in a disordered network of boron dopant atoms in silicon is used to perform nonlinear classification and feature extraction.