Defect Level Switching for Highly-Nonlinear and Hysteretic Electronic Devices

Nonlinear and hysteretic electrical devices are needed for applications from circuit protection to next-generation computing. Widely-studied devices for resistive switching are based on mass transport, such as the drift of ions in an electric field, and on collective phenomena, such as insulator-metal transitions. We ask whether the large photoconductive response known in many semiconductors can be stimulated in the dark and harnessed to design electrical devices. We design and test devices based on photoconductive CdS, and our results are consistent with the hypothesis that resistive switching arises from point defects that switch between deep- and shallow-donor configurations: defect level switching (DLS). This new electronic device design principle - photoconductivity without photons - leverages decades of research on photoconductivity and defect spectroscopy. It is easily generalized and will enable the rational design of new nonlinear, hysteretic devices for future electronics.

A Deep Convolutional Neural Network to Analyze Position Averaged Convergent Beam Electron Diffraction Patterns

We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data. With the aligned data, additional networks then measure the sample thickness and tilt. The performance of the network is explored as a function of a variety of variables including thickness, tilt, and dose. A methodology to explore the response of the neural network to various pattern features is also presented. Processing patterns at a rate of $sim$0.1 s/pattern, the network is shown to be orders of magnitude faster than a brute force method while maintaining accuracy. The approach is thus suitable for automatically processing big, 4D STEM data. We also discuss the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. The source code is available at https://github.com/subangstrom/DeepDiffraction.