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A Fully Si-Compatible Ni/Si3N4/Al2O3/p + Poly-Si RRAM Device for Analog Synapse and Its System-Level Assessment
Yeji Lee1, Soomin Kim2, Seongmin Kim3
1Department of Semiconductor Engineering, Ewha Womans University, Seoul 03670, South Korea.
ACS Omega
|December 8, 2025
Summary
We developed a silicon-compatible resistive-switching random-access memory (RRAM) device for processing-in-memory (PIM) applications. This device shows stable switching, high endurance, and excellent analog synaptic behavior, enabling efficient AI hardware.
Area of Science:
- Materials Science
- Electrical Engineering
- Computer Science
Background:
- Processing-in-memory (PIM) architectures require nonvolatile memory devices with analog capabilities.
- Existing solutions often face challenges with silicon compatibility, endurance, and energy efficiency.
Purpose of the Study:
- To propose and characterize a novel, fully Si-compatible resistive-switching random-access memory (RRAM) device for PIM applications.
- To evaluate the device's potential as an analog synaptic device for neural network computing.
Main Methods:
- Fabrication of a Ni/Si3N4/Al2O3/p+ poly-Si RRAM device with CMOS process integration.
- Characterization of electrical switching properties, including endurance, retention, and analog conductance modulation.
- Implementation and simulation of a VGG-8 convolutional neural network using NeuroSim 1.4.
Main Results:
- The device exhibited stable bipolar switching with a high on/off ratio (~10^4) and over 10^7 switching operations.
- Excellent retention (>10^5 s) and linear, symmetric analog conductance modulation for synaptic applications were achieved.
- A VGG-8 neural network simulation demonstrated high inference accuracy (90.25%) with low latency and high energy efficiency.
Conclusions:
- The proposed Si-compatible RRAM device is suitable for energy-efficient PIM and analog neural network computing.
- The device's characteristics pave the way for next-generation nonvolatile memory and AI hardware.
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