All‐Optically Controlled Retinomorphic Memristor for Image Processing and Stabilization

Abstract

Image stabilization is a crucial field in machine vision, aiming to eliminate image blurring or distortion caused by the camera or object jitter. However, traditional image stabilization techniques often suffer from the drawbacks of requiring complex equipment or extensive computing resources, resulting in inefficiencies. In contrast, the human retina performs a highly efficient all‐in‐one system, encompassing the detection and processing of light stimuli. In this study, an all‐optically controlled retinomorphic memristor based on the CsxFAyMA1‐x‐yPb(IzBr1‐z)3 is proposed, which integrates perception, storage, and processing functions. This memristor exhibits significant advantages in image stabilization. It is capable of positively and negatively modulating its conductance using specific intensities (11.8 and 0.9 mW cm−2, respectively) of red light (630 nm). To demonstrate the effectiveness of the proposed approach, handwritten digit recognition simulations are conducted. The application of specific light stimuli effectively highlights the characteristics of blurred images. The processed images are then fed into a conductance‐mapped neural network for rapid recognition. Remarkably, the recognition rates of the processed images reach 83.5% after 19 000 iterations, surpassing the performance of blurred images (only 56.2% after 19 000 iterations). These results highlight the immense potential of retinomorphic memristors as the hardware foundation for next‐generation image stabilization systems.

PBDB-T/Pentacene-Based Organic Optoelectronic Synaptic Transistor with Adjustable Critical Flicker Fusion Frequency for Dynamic Vision

“…Nonlinearity, maximum conductance ( G max ) and minimum conductance ( G min ) were extracted from the data, as presented in Figure S11 (Supporting Information) and Table S2 (Supporting Information). We simulated an ANN based on the Modified National Institute of Standards and Technology (MNIST) data set of handwritten digits. , ANN has a three-layer neural network with 28 × 28 input neurons, 100 hidden neurons, and 10 output neurons, as shown in Figure e Figure f shows the recognition rates obtained under three cFFF modes.…”

“…We simulated an ANN based on the Modified National Institute of Standards and Technology (MNIST) data set of handwritten digits. 35,36 ANN has a three-layer neural network with 28 × 28 input neurons, 100 hidden neurons, and 10 output neurons, as shown in Figure 4e. 37 Figure 4f shows the recognition rates obtained under three cFFF modes.…”

High synaptic plasticity enabled by controlled ion migration in organic heterojunction memristors

“…An artificial neural network (ANN) was simulated based on the Modified National Institute of Standards and Technology (MNIST) dataset of handwritten digits. 50,51 The ANN comprises a three-layer neural network with 28 × 28 input neurons, 100 hidden neurons, and 10 output neurons, 52 depicted in Fig. 4e.…”

Asymmetric-Resistive-Switching Device with Reconfigurable Synaptic Functions for Logic-In-Memory

“…As a new type of electronic component, the memristor has attracted extensive attention in recent year. They are broadly used in modeling, logic circuit design, information storage, image processing, chaotic circuits, etc. − …”