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.

许多纳米级设备需要精确的优化才能发挥作用。当端子和耦合的数量增加时，将它们调整到所需的工作状态变得越来越困难和耗时。缺陷和设备之间的差异会阻碍基于物理模型的优化。深度神经网络（DNN）可以对各种复杂的物理现象进行建模，但到目前为止，它主要用作预测工具。在这里，我们提出了一种通用的深度学习方法，以有效地优化复杂的多端子纳米电子器件以获得所需的功能。我们演示了在硅中掺杂原子无序网络中实现功能的方法。我们使用DNN对设备的输入输出特性进行建模，然后通过梯度下降优化DNN模型中的控制参数，以实现各种分类任务。当将相应的控制设置应用于物理设备时，得到的功能与DNN模型所预测的一样。我们希望我们的方法有助于复杂的（量子）纳米电子器件的快速，原位优化。