A deep-learning approach to realizing functionality in nanoelectronic devices
A deep-learning approach to realizing functionality in nanoelectronic devices
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DOI：
10.1038/s41565-020-00779-y
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发表时间：
2020-10-19
影响因子：
38.3
通讯作者：
van der Wiel, Wilfred G.
中科院分区：
文献类型：
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作者：
Ruiz Euler, Hans-Christian;Boon, Marcus N.;van der Wiel, Wilfred G.
随着设备复杂性的不断增加，纳米级和量子电子设备的功能实现和优化变得越来越具有挑战性。利用物理设备响应训练深度神经网络并搜索数字设备中的功能可以缓解这一挑战。许多纳米级设备需要精确优化才能发挥作用。当端子和接头数量增加时，将它们调整到所需的操作状态变得越来越困难和耗时。缺陷和设备之间的差异阻碍了使用基于物理的模型的优化。深度神经网络 (DNN) 可以对各种复杂的物理现象进行建模，但迄今为止主要用作预测工具。在这里，我们提出了一种通用的深度学习方法来有效优化复杂的多终端纳米电子设备以获得所需的功能。我们展示了在硅中掺杂原子无序网络中实现功能的方法。我们用DNN对设备的输入输出特性进行建模，随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。当相应的控制设置应用于物理设备时，所产生的功能如 DNN 模型所预测的那样。我们希望我们的方法有助于复杂（量子）纳米电子器件的快速原位优化。
Function implementation and optimization in nanoscale and quantum-electronic devices become increasingly challenging with the growing complexity of the devices. Training a deep neural network with the physical device response and searching for the functionality in the digital device can ease this challenge.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.