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Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
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Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications.

Atul C Khot1, Tukaram D Dongale2, Kiran A Nirmal1

  • 1School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.

ACS Applied Materials & Interfaces
|February 18, 2022
PubMed
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Amorphous boron nitride (a-BN) enables advanced multilevel resistive switching (MRS) for neuromorphic computing. This CMOS-compatible memristive device shows high endurance and mimics synaptic plasticity for neural networks.

Keywords:
2D electronicsamorphous boron nitride (a-BN)memristive effectmultilevel resistive switchingneuromorphic computingsynaptic learningtime-series analysis

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Area of Science:

  • Materials Science
  • Nanoelectronics
  • Solid-State Physics

Background:

  • Two-dimensional (2D) nanomaterials offer potential for memory and synaptic devices.
  • Challenges remain in process compatibility, synthesis reliability, and cost-effectiveness of 2D materials.
  • Amorphous boron nitride (a-BN) is a promising candidate for 2D nanoelectronics.

Purpose of the Study:

  • To explore the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications.
  • To fabricate and characterize a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device.
  • To investigate the resistive switching mechanism and memristive behavior of the a-BN device.

Main Methods:

  • Fabrication of a CMOS-compatible Ag/a-BN/Pt memory device.
  • Characterization of the a-BN switching layer using various analyses.
  • Electrical testing for bipolar resistive switching, MRS, endurance, and retention.
  • Time-series analysis (Holt's exponential smoothing) for switching voltage modeling.
  • Conductive atomic force microscopy (CAFM) to elucidate the switching mechanism.
  • Neural network simulations to evaluate synaptic learning capabilities.

Main Results:

  • The Ag/a-BN/Pt device exhibited bipolar resistive switching with low set/reset voltages (+0.8 V/-2 V).
  • The device demonstrated multilevel states with uniform resistance distribution, high endurance (10^4 DC cycles), and long retention (>10^6 s).
  • A mixed electrochemical metallization and valence change mechanism was identified.
  • The device successfully mimicked synaptic plasticity (potentiation/depression, STDP) with 90.8% pattern accuracy in neural network simulations.

Conclusions:

  • Amorphous boron nitride is a viable material for CMOS-compatible memristive devices.
  • The Ag/a-BN/Pt device shows significant potential for multilevel memory and neuromorphic computing applications.
  • The study clarifies the resistive switching mechanism and demonstrates the device's ability to emulate synaptic learning rules.