Classification with a disordered dopant-atom network in silicon

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 density⁵, 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 up⁷. Here we use a parallel, nanoscale approach inspired by filters in the brain¹ and artificial neural networks² to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction^9–11 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 gates¹² up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters² 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 data¹³. Our results establish a paradigm of silicon-based electronics for small-footprint and energy-efficient computation¹⁴.