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All-Ferroelectric Memtransistors for Brain-Inspired Computing

  • Jinhua Zeng
  • , Chenyu Ye
  • , Guangjian Wu*
  • , Huiting Wang
  • , Qianru Zhao
  • , Shuaiqin Wu
  • , Xudong Wang*
  • , Tie Lin
  • , Jun Ge
  • , Hong Shen
  • , Junhao Chu
  • , Jianlu Wang*
  • *Corresponding author for this work
  • CAS - Shanghai Institute of Technical Physics
  • University of Chinese Academy of Sciences
  • Fudan University

Research output: Contribution to journalArticlepeer-review

Abstract

In-memory computing is pursued to overcome the memory and power walls inherent to the von Neumann architecture. However, heterosynaptic memtransistors with higher modulation dimensionality and enhanced memory capability still suffer from a limited conductance dynamic range and few gate-controlled states, constraining learning precision. Here, an all-ferroelectric memtransistor is demonstrated that synergistically combines a P(VDF-TrFE) ferroelectric gate dielectric with an α-In2Se3 ferroelectric semiconductor channel. As the third-terminal modulator, the P(VDF-TrFE) gate sets the channel Fermi level via out-of-plane polarization reversal, while the channel's in-plane polarization at the pre- and post-synaptic drain and source asymmetrically tunes the contact Schottky barriers. The coupling of these two distinct ferroelectric effects generates four well-separated nonvolatile conductance states in fully polarized configurations, introduces 12 third-terminal states via ferroelectric-gate domain control, and enables 100 intermediate states in the ferroelectric channel through source–drain pulses. The device emulates heterosynaptic regulation, enabling global enhancement or suppression of synaptic features. Compared with conventional designs, it offers a dynamic range of up to 331.91 and 12 gate-controlled states. An adaptive neural network implemented with measured device characteristics achieves 95.68% pattern recognition accuracy, with gate pulses selecting optimal operational regimes. This work provides an effective device platform for high-performance brain-inspired computing.

Original languageEnglish
Article numbere31393
JournalAdvanced Functional Materials
Volume36
Issue number34
DOIs
StatePublished - 27 Apr 2026
Externally publishedYes

Keywords

  • adaptivity
  • all-ferroelectric memtransistors
  • brain-inspired computing
  • heterosynaptic plasticity
  • in-plane and out-of-plane polarizations

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