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Implementation of a Nonlinear Microscope Based on Stimulated Raman Scattering

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High-Linearity Ta₂O₅ Memristor and Its Application in Gaussian Convolution Image Denoising.

Yucheng Wang¹, Hexin Wang², Dingyun Guo²

¹Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China.

ACS Applied Materials & Interfaces

|August 27, 2024

Summary

This study introduces a novel memristor device for efficient Gaussian filtering in image processing. By utilizing a W/Ta2O5/ZnO/Ag memristor array, computational overhead is reduced, demonstrating potential for convolutional neural networks (CNNs).

Keywords:

Gaussian convolution filtering Ta2O5-based memristors convolutional neural networks heterojunction image denoising

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Area of Science:

Materials Science
Computer Engineering
Image Processing

Background:

Gaussian filtering in image processing involves computationally intensive convolution operations.
Traditional methods using Gaussian matrices strain system memory due to extensive multiplications and additions.

Purpose of the Study:

To develop a hardware-based solution for efficient Gaussian filtering, reducing computational load.
To explore the application of memristor devices in image convolution operations.

Main Methods:

A W/Ta2O5/Ag memristor was fabricated and subsequently modified with a ZnO interlayer.
The Ta2O5/ZnO heterostructure's linear pulse response was leveraged for conductance modulation.
A 5x5 memristor array was assembled to act as a convolution kernel for Gaussian noise removal.

Main Results:

The W/Ta2O5/ZnO/Ag bilayer memristor demonstrated improved linearity in pulse response.
Memristor array-based denoising achieved results comparable to Gaussian matrix convolution, with an average loss of less than 5%.
The memristor array effectively performed Gaussian noise removal in image processing.

Conclusions:

Memristor devices offer a promising approach to mitigate computational overhead in convolution operations.
The developed memristor array shows significant potential for image processing tasks, including applications in convolutional neural networks (CNNs).