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Related Concept Videos

Non-ohmic Devices00:51

Non-ohmic Devices

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In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
Consider a simple circuit consisting of a battery, a diode, and a resistor. A...
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One-Step Annealing-Configured Hf0.2Zr0.8O2 Memristive-Antiferroelectric Devices for Bioinspired CSNN Neuromorphic

Jinhao Zhang1,2, Kangli Xu3, Chen Lu3

  • 1Shandong Key Laboratory of Next-Generation Semiconductor Technology and Systems, School of Integrated Circuits, Shandong University, Jinan 250100, China.

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|December 4, 2025
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Summary

This study introduces a novel CMOS-compatible Hf0.2Zr0.8O2 platform for hybrid neuromorphic computing. It enables dual-mode artificial neuron and synaptic devices, achieving high accuracy in convolutional spiking neural networks.

Keywords:
CMOS compatibilityantiferroelectric deviceconvolutional spiking neural networkmemristorneuromorphic computing

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Traditional computing architectures face limitations due to separated components, hindering the development of efficient neuromorphic systems.
  • Developing compatible artificial neuron and synaptic devices is crucial for advancing hybrid neuromorphic computing.
  • Existing solutions often lack CMOS compatibility, limiting integration with standard semiconductor manufacturing processes.

Purpose of the Study:

  • To present a single-stack, CMOS-compatible Hf0.2Zr0.8O2 platform for fabricating both memristive synapse and antiferroelectric neuron devices.
  • To demonstrate the dual-mode functionality of the TiN/Hf0.2Zr0.8O2/TiN stack for neuromorphic applications.
  • To construct and evaluate a convolutional spiking neural network using these integrated devices.

Main Methods:

  • Fabrication of a single-stack TiN/Hf0.2Zr0.8O2/TiN structure using CMOS-compatible processes.
  • Utilizing the as-deposited Hf0.2Zr0.8O2 film as a memristive synapse for analog conductance modulation.
  • Performing a one-step postdeposition anneal to transform the same stack into an antiferroelectric neuron device exhibiting spontaneous depolarization.
  • Integrating the dual-mode devices into a convolutional spiking neural network architecture.

Main Results:

  • The Hf0.2Zr0.8O2 platform successfully yielded both memristive synaptic and antiferroelectric neuronal functionalities from the same material stack.
  • Memristive devices demonstrated analog conductance modulation suitable for convolutional feature extraction.
  • Antiferroelectric devices exhibited spontaneous depolarization, enabling spike-based encoding and biologically plausible neuronal dynamics.
  • The integrated convolutional spiking neural network achieved a high accuracy of 97.9% in dynamic gesture recognition.

Conclusions:

  • The presented CMOS-compatible, dual-mode device platform offers a unified material solution for hybrid neuromorphic computing.
  • This approach facilitates the creation of compact neuromorphic hardware by leveraging process-compatible manufacturing.
  • The successful implementation in a convolutional spiking neural network highlights the potential for advanced AI applications using these neuromorphic electronics.