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Zhijian Zhong1, Zhiguo Jiang1, Jianning Huang1

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Memristors offer potential for neuromorphic chips, mimicking brain efficiency and computation.
  • Conventional resistive switching (RS) in memristors faces limitations in ON/OFF states and high switching voltage, hindering low-power emulation of biological synapses.

Purpose of the Study:

  • To introduce a 'stateful' threshold switching (TS) mechanism operating in the millivoltage range.
  • To demonstrate the implementation of fundamental neuromorphic learning using a single memristor with four resistive states.
  • To achieve multifunctional associative learning with ultralow power consumption.

Main Methods:

  • Utilizing the charging/discharging parasitic elements of a memristive circuit to achieve 'stateful' TS.
  • Implementing neuromorphic learning functions including metaplasticity and associative learning (acquisition, extinction, recovery, generalization, protective inhibition).
  • Operating the device with nonpolar switching and measuring power consumption.

Main Results:

  • Demonstrated 'stateful' threshold switching (TS) in the millivoltage range, dependent on resistive switching (RS) states.
  • Successfully implemented fundamental neuromorphic learning and complex associative learning behaviors using a single memristor.
  • Achieved ultralow power consumption of 5.71 pW for multifunctional associative learning.

Conclusions:

  • The 'stateful' TS memristor enables flexible tunability, enriched states, and ultralow operating voltage for neuromorphic applications.
  • This approach offers new pathways for developing massive storage units and bio-inspired neuromorphic systems.
  • The demonstrated capabilities pave the way for more efficient and brain-like computing systems.