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Laterally Gated CuInP2S6 Ferroelectric Field Effect Transistors for Neuromorphic Computing
Youna Huang1,2,3, Linkun Wang1,2,3, Fengyuan Zhang1,3
1Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
ACS Applied Materials & Interfaces
|July 4, 2025
Summary
This study introduces novel 2D laterally gated ferroelectric field-effect transistors (LG-FeFETs) for advanced AI. These devices offer multilevel data processing and tunable synaptic functions, outperforming traditional transistors for neuromorphic computing.
Area of Science:
- Materials Science
- Nanotechnology
- Computer Engineering
Background:
- The increasing demand for artificial intelligence (AI) drives the need for efficient data storage and neuromorphic computing.
- Ferroelectric field-effect transistors (FeFETs) offer a potential solution to the von Neumann architecture bottleneck by integrating memory and processing.
- Laterally gated FeFETs (LG-FeFETs) provide advantages like low leakage current and reduced device height.
Purpose of the Study:
- To develop and characterize two-dimensional (2D) laterally gated FeFETs (LG-FeFETs) using a van der Waals heterostructure of ferroelectric CuInP2S6 (CIPS) and MoS2.
- To investigate the multilevel data processing capabilities and tunable synaptic functions of these 2D LG-FeFETs.
- To evaluate the performance of these devices in neuromorphic computing applications, including neural network training and reservoir computing.
Main Methods:
- Fabrication of 2D LG-FeFET devices using CIPS and MoS2 in a van der Waals heterostructure.
- Electrical characterization to measure memory window, leakage current, and on/off ratio.
- In situ piezoresponse force microscopy (PFM) to analyze polarization dynamics.
- Implementation of devices for online training of neural networks and reservoir computing.
Main Results:
- The 2D LG-FeFETs demonstrated a large memory window (10 V), low leakage current (<0.01 nA), and a high on/off ratio (10^5).
- Devices successfully emulated synaptic plasticity (long-term and short-term) under electrical stimuli.
- High recognition accuracy (97.4%) was achieved for handwritten digits using the synaptic device for neural network training.
- Reservoir computing for image classification was demonstrated based on the device's short-term plasticity.
Conclusions:
- The developed 2D LG-FeFETs show significant potential for high-density data processing systems.
- These devices are promising candidates for next-generation neuromorphic computing applications.
- The study highlights the control of multiple conductance states by polarization evolution dynamics in 2D LG-FeFETs.
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