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3-bit multilevel operation with accurate programming scheme in TiO/Al2O3memristor crossbar array for quantized

Tae-Hyeon Kim1, Jaewoong Lee1, Sungjoon Kim1

  • 1Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul 08826, Republic of Korea.

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|March 22, 2021
PubMed
Summary

This study presents a novel reset-voltage-control programming scheme for memristor crossbar arrays, enabling precise 3-bit multilevel operations crucial for efficient neuromorphic computing hardware. The developed system achieved high accuracy in pattern recognition tasks.

Keywords:
RRAMTiO x /Al2O3 memristormultilevel operationneuromorphic systemoff-chip trainingsynaptic deviceweight quantizationweight transfer

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Artificial Intelligence Hardware

Background:

  • Rapid advancements in artificial intelligence (AI) have led to complex algorithms and network structures, resulting in significant power consumption challenges due to extensive computational demands.
  • Neuromorphic computing, utilizing hardware AI technologies with integrated memory devices, offers a promising solution to mitigate these power consumption issues.
  • Accurate multilevel operations in synaptic devices are essential for mimicking the floating-point weight values used in software-based AI, and efficient off-chip training methods are required for weight transfer.

Purpose of the Study:

  • To fabricate and evaluate a 32x32 memristor crossbar array capable of precise 3-bit multilevel operations.
  • To verify the programming accuracy of a proposed reset-voltage-control scheme for TiO2/Al2O3-based memristors.
  • To construct a neuromorphic system for pattern recognition and assess the impact of programming errors on classification accuracy.

Main Methods:

  • Fabrication of a 32x32 memristor crossbar array using TiO2/Al2O3 materials.
  • Implementation of a reset-voltage-control programming scheme to achieve 3-bit quantized levels.
  • Construction of a synapse using differential memristors and a fully-connected neural network for Modified National Institute of Standards and Technology (MNIST) pattern recognition.
  • Post-training quantization of weights considering the 3-bit characteristics of the memristors and verification of classification accuracy using measured data.

Main Results:

  • Successfully verified 3-bit multilevel operations with high programming accuracy (1.79% root-mean-square error).
  • Achieved 98.12% classification accuracy on MNIST data using the developed neuromorphic system.
  • Demonstrated the effectiveness of the reset-voltage-control programming scheme for precise weight tuning in memristor-based systems.

Conclusions:

  • The proposed reset-voltage-control programming scheme enables precise tuning of memristor synaptic devices.
  • The developed neuromorphic system shows high potential for efficient and accurate pattern recognition.
  • This research is expected to contribute significantly to the development of highly precise neuromorphic systems.