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Related Experiment Video

Updated: May 9, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Reconfigurable Adaptive Synapse and Logic Device by Ambipolar Ferroelectric Semiconductor.

Jaewook Yoo¹, Minah Park¹, Seokjin Oh¹

¹Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)

|May 8, 2026

Summary

This study introduces an alpha-In2Se3 device with ambipolar transport for dynamic AI hardware control. It achieves adaptive synaptic and logic functions with ultra-low energy consumption, paving the way for integrated neuromorphic computing.

Keywords:

ferroelectric semiconductor reconfigurable synapse surface charge transfer doping tunable logic device two‐dimensional material

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Area of Science:

Materials Science
Artificial Intelligence Hardware
Semiconductor Devices

Background:

Dynamic control is crucial for integrating learning and computing in AI hardware.
Existing methods for dynamic mode modulation face implementation challenges due to complex structures and external signals.

Purpose of the Study:

To present a novel alpha-In2Se3 device enabling dynamic control for AI hardware.
To demonstrate adaptive synaptic and logic functions with energy efficiency.

Main Methods:

Utilized surface charge transfer doping (SCTD) to achieve ambipolar transport in alpha-In2Se3.
Leveraged ferroelectric polarization for adaptive synaptic and logic operations.
Investigated p-type conduction mechanism involving In2O3 and selenium vacancies.

Main Results:

Achieved reversible ferroelectric switching for tunable inverter trip points.
Demonstrated excitatory/inhibitory synaptic mode reconfiguration at ultra-low energy consumption (200 aJ to 20 fJ).
The alpha-In2Se3 device exhibited ambipolar transport behavior.

Conclusions:

The developed alpha-In2Se3 device offers a practical approach to dynamic control in AI hardware.
This work advances the integration of neuromorphic computing and logic functionalities in a single device.
The study moves towards prototype devices for dynamic, non-stationary AI applications.