High-Linearity Ta 2 O 5 Memristor and Its Application in Gaussian Convolution Image Denoising

• Published in: ACS applied materials & interfaces 2024-09, Vol.16 (36), p.47879-47888
• Main Authors: Wang, Yucheng, Wang, Hexin, Guo, Dingyun, An, Zeyang, Zheng, Jiawei, Huang, Ruixi, Bi, Antong, Jiang, Junyu, Wang, Shaoxi
• Format: Article
• Language: English
• ISSN: 1944-8244; 1944-8252
• DOI: 10.1021/acsami.4c09056

In the image Gaussian filtering process, convolving with a Gaussian matrix is essential due to the numerous arithmetic computations involved, predominantly multiplications and additions. This can heavily tax the system's memory, particularly with frequent use. To address this issue, a W/Ta O /Ag memristor was employed to substantially mitigate the computational overhead associated with convolution operations. Additionally, an interlayer of ZnO was subsequently introduced into the memristor. The resulting Ta O /ZnO heterostructure layer exhibited improved linearity in the pulse response, which enhanced linearity facilitates easy adjustment of the conductance magnitude through a linear mapping of the number of pulses and the conductance. Subsequently, the conductance of the W/Ta O /ZnO/Ag bilayer memristor was employed as the weights for the convolution kernel in convolution operations. Gaussian noise removal in image processing was achieved by assembling a 5 × 5 memristor array as the kernel. When denoising was performed using memristor arrays, compared to denoising achieved through Gaussian matrix convolution, an average loss of less than 5% was observed. The provided memristors demonstrate significant potential in convolutional computations, particularly for subsequent applications in convolutional neural networks (CNNs).