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February 2, 2026Advanced Electronic Materials2 citationsOpen Access

Reconfigurable, Non‐Volatile Switching in WO 3 Film for Resistive Memory and Multistate Programming Toward Energy‐Efficient Neuromorphic Computing Applications

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KHKeval HadiyalNRNagarajan RaghavanRTR. Thamankar

Key Points

  • The aim is to evaluate a tungsten oxide based memristive device for efficient neuromorphic computing applications.
  • Developed a WO3 based resistive switching memory device
  • Assessed stability over 12,10 dc-switching cycles
  • Conducted voltage stress measurements for low voltage operation
  • Evaluated synaptic characteristics like paired pulse facilitation and depression
  • Achieved retention of over 5000 seconds at +0.2 V
  • Obtained large ON/OFF ratio of 10^3
  • Demonstrated low energy consumption of 2.1 per event
  • Recognition accuracy of 88% with the MNIST dataset
  • Transition from short term memory to long term memory observed.

Abstract

ABSTRACT Non‐volatile memristive device compatible for futuristic memory, data storage and in‐memory computing with good exceptional energy efficiency will be an integral part of neuromorphic architecture. Tungsten oxide () is a versatile metal oxide displaying memristive characteristics where resistance states can be controlled through oxygen vacancy concentration holds great potential for such low energy neuromorphic devices. Here, we report a WO 3 based resistive switching memory device showing exceptional stability in switching with respect to number of dc‐switching cycles (12 10 cycles) and retention for more than 5000 sec at +0.2 V. Based on voltage stress measurements the device offers low voltage switching operation (+0.72 SET V, –0.12 RESET V), a large ON/OFF ratio (10 3 ), low energy consumption (2.1 per event, and dynamic range of 7. Additionally, primary synaptic characteristics such as paired pulse felicitation (PPF) and paired pulse depression (PPD) are shown which indicates that the based devices are suitable to neuromorphic applications. Interestingly, a transition between short term memory (STM) to long term memory (LTM) is seen as a function of stimulation duration. The learning and forgetting curves show very good linearity with image recognition capability using the MNIST data set. Recognition accuracy of 88% is achieved with respect to ideal device. This work demonstrates the effective use of WO 3 based memristive device for low energy consuming neuromorphic computing applications.

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Cite This Study

Hadiyal et al. (2026) studied this question.

synapsesocial.com/papers/6980feeac1c9540dea81163ehttps://doi.org/10.1002/aelm.202500658
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