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Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices.

Chih-Yang Lin1, Jia Chen2, Po-Hsun Chen3,4

  • 1Department of Physics, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan.

Small (Weinheim an Der Bergstrasse, Germany)
|September 30, 2020
PubMed
Summary

This study demonstrates a novel resistive random-access memory (RRAM) device mimicking biological synapses. The device shows potential for creating efficient neuromorphic computing systems by emulating synaptic plasticity and memory pruning.

Keywords:
lithiumneuromorphic computingpaired pulse facilitation (PPF)resistive random access memory (RRAM)spike-timing-dependent plasticity (STDP)synaptic plasticity

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Biologically plausible computing requires precise control over analog synaptic characteristics.
  • Existing neuromorphic computing systems need advanced synaptic devices for efficient neural network simulation.

Purpose of the Study:

  • To demonstrate a lithium-doped silicate resistive random-access memory (RRAM) device with a titanium nitride (TiN) electrode that mimics biological synapses.
  • To investigate the device's capability for emulating synaptic plasticity, memory functions, and learning rules found in the human brain.

Main Methods:

  • Fabrication of lithium-doped silicate RRAM devices with TiN electrodes.
  • Characterization of device behavior, including forming, filamentary retraction, and resistance decay.
  • Emulation of synaptic plasticity mechanisms like spike-timing-dependent plasticity (STDP) and paired-pulse facilitation.
  • Demonstration of short-term and long-term memory emulation and synaptic pruning.

Main Results:

  • The RRAM device exhibits controllable forming and filament retraction due to the low ionization energy of lithium ions.
  • TiN electrodes facilitate reliable state-dependent decay, enabling multi-bit functionality and synaptic plasticity.
  • The device successfully emulates both short-term and long-term memory across various timescales.
  • Mechanisms for self-pruning and time-dependent resistance decay were observed, mimicking biological synaptic pruning.

Conclusions:

  • The developed RRAM device effectively mimics biological synapses, offering multi-bit functionality and synaptic plasticity.
  • The device's ability to emulate learning rules like STDP and synaptic pruning is crucial for advancing neuromorphic computing.
  • This technology holds significant potential for developing highly efficient and brain-inspired computing systems.