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.

Classification with a disordered dopant-atom network in silicon.

Classification is an important task at which both biological and artificial neural networks excel. In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable, simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers.