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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Memristors Based on Ferroelectric Cu-Deficient Copper Indium Thiophosphate for Multilevel Storage and Neuromorphic
Mengdie Li1, Yanyan He1, Chengyang Wang1
1College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
Small (Weinheim an Der Bergstrasse, Germany)
|June 23, 2025
Summary
Copper-deficient copper indium thiophosphate (CIPS*) exhibits stable resistive switching behaviors for advanced memory applications. This 2D ferroelectric material enables multilevel storage and neuromorphic computing with high accuracy.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Nanotechnology
Background:
- Understanding ferroelectricity and ionic activity in 2D materials is crucial for device performance.
- Copper indium thiophosphate (CIPS) shows Cu+ migration and conductive filament formation, influencing its properties.
- Cu-deficient CIPS (CIPS*) is investigated to explore novel resistive switching (RS) behaviors.
Purpose of the Study:
- To investigate the resistive switching characteristics of Cu-deficient CIPS (CIPS*) for potential memristor applications.
- To explore the digital and analog resistive switching behaviors and their underlying mechanisms.
- To evaluate the performance of CIPS*-based memristors in multilevel storage and neuromorphic computing.
Main Methods:
- Fabrication of Cu-deficient CIPS (CIPS*) thin films.
- Characterization of resistive switching properties, including digital and analog behaviors.
- Device fabrication and testing for endurance, ON/OFF ratio, and synaptic functions.
- Implementation of an artificial neural network for handwritten digit recognition.
Main Results:
- CIPS* demonstrates stable non-volatile digital and analog resistive switching.
- The formation of metallic In4/3P2S6 (IPS) in the low-resistance state leads to a high ON/OFF ratio (5 × 10^5) and endurance (>2000 cycles).
- Simulations show amplitude-dependent and polarity-independent long-term potentiation and depression.
- A CIPS*-based artificial neural network achieved 91.15% accuracy in handwritten digit recognition, maintaining 90.71% with variations.
Conclusions:
- Cu-deficient CIPS (CIPS*) is a promising 2D ferroelectric material for reliable memristors.
- The observed resistive switching behaviors are suitable for multilevel data storage.
- CIPS*-based synaptic memristors show significant potential for neuromorphic computing applications.
- This research provides insights into modulating device performance through controlled material deficiency.
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