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A Flexible Laser-Induced Graphene Memristor with Volatile Switching for Neuromorphic Applications
Mohit D Ganeriwala1, Roberto Motos Espada1, Enrique G Marin1
1Electronics Department, Campus Fuentenueva S/N, University of Granada, Granada 18071, Spain.
ACS Applied Materials & Interfaces
|September 6, 2024
Summary
Researchers developed a simple, one-step laser process to create flexible graphene memristors for neuromorphic computing. This novel method enables volatile resistive switching without extra materials, paving the way for advanced artificial synapses and neurons.
Area of Science:
- Materials Science
- Nanotechnology
- Neuroscience
Background:
- Graphene and related materials are crucial for neuromorphic computing, mimicking biological synapses and neurons with memristive devices.
- Current fabrication methods are complex, high-temperature, multi-step chemical processes on rigid substrates, limiting accessibility.
Purpose of the Study:
- To demonstrate a simplified, cost-effective method for fabricating graphene-based memristors on flexible substrates.
- To report, for the first time, volatile resistive switching in laser-induced graphene (LIG) without additional materials.
Main Methods:
- Utilized a commercial laser for a one-step fabrication of laser-induced graphene (LIG) memristors.
- Employed a flexible polyimide substrate for direct device fabrication.
- Investigated the resistive switching characteristics and suitability for neuromorphic applications.
Main Results:
- Successfully fabricated LIG memristors on a flexible substrate using a single-step laser process.
- Observed a novel volatile resistive switching phenomenon in LIG without precursor materials.
- Demonstrated multilevel resistance switching with high endurance and tunable timing.
Conclusions:
- The one-step LIG fabrication offers a simplified, cost-effective, and controllable approach for neuromorphic device development.
- The demonstrated volatile switching and tunable characteristics make LIG memristors suitable for synaptic elements and artificial neurons.
- This advancement facilitates broader experimental exploration in flexible neuromorphic computing.
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