Implementation of Convolutional Kernel Function Using 3-D TiOx Resistive Switching Devices for Image Processing

We implemented a convolution operation in a convolutional neural network by introducing a 3-D integrated TiO<sub><italic>x</italic></sub>-based resistive switching device (RSD) array to act as the kernel. The 3-D integrated TiO<sub><italic>x</italic></sub>-based RSD exhibits a gradual SET/RESET behavior, which enables multilevel characteristics. A pair of layers in the 3-D structured array is used to achieve the positive and negative weights. Based on these results, we demonstrated the <inline-formula> <tex-math notation="LaTeX">$\textsf {3} \times \textsf {3}$ </tex-math></inline-formula> Laplace kernel for edge detection using images from the Columbia Object Image Library data set in a simulator to validate the convolution operation.

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