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Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor
Sung Keun Shim1, Keonuk Lee1, Janguk Han1
1Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
Advanced Materials (Deerfield Beach, Fla.)
|October 1, 2024
Summary
This study introduces a novel tunable thresholding function using memristors and a metal-oxide-semiconductor capacitor for precise event detection in time-series data. The new hardware kernel demonstrates high accuracy in analyzing electrocardiograms, classifying spoken digits, and enabling biometric authentication.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Signal Processing
Background:
- Precise event detection in time-series data is crucial, especially in noisy environments.
- Reservoir computing with memristive devices excels at temporal signal processing but lacks intrinsic thresholding.
- Existing methods struggle with the nuanced requirements of event detection in complex datasets.
Purpose of the Study:
- To develop a novel hardware kernel with tunable thresholding capabilities for enhanced event detection.
- To integrate memristors and metal-oxide-semiconductor capacitors for a robust computing system.
- To demonstrate the efficacy of this system in diverse applications including biosignal analysis and pattern recognition.
Main Methods:
- Implementation of a 2-memristor, 1-metal-oxide-semiconductor capacitor (2M1MOS) kernel system using Pt/HfO2/TiN (PHT) memristors and Ni/HfO2/n-Si (NHS) capacitor.
- Leveraging the nonlinear current-voltage characteristics of memristors and capacitance-voltage characteristics of the capacitor for tunable thresholding.
- Utilizing capacitive thresholding to record feature-specified information from input signals onto memristors.
Main Results:
- The memristive response showed over a tenfold difference between normal and arrhythmia beats in electrocardiogram (ECG) analysis.
- Isolated spoken digit classification achieved a low error rate of 0.7% by tuning thresholds.
- Successful application in biometric authentication using heartbeats and voice data as bio-indicators with varied threshold times.
Conclusions:
- The proposed 2M1MOS kernel effectively implements tunable thresholding for precise event detection in memristive systems.
- Heterogeneous integration of memristors and capacitors offers a promising framework for advanced signal processing applications.
- This approach highlights the potential of thresholding computing in memristive devices for complex data analysis and security.
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