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Nano-ionic Solid Electrolyte FET-Based Reservoir Computing for Efficient Temporal Data Classification and

Ankit Gaurav1, Xiaoyao Song2, Sanjeev Kumar Manhas1

  • 1Indian Institute of Technology Roorkee, Roorkee, 247667, India.

ACS Applied Materials & Interfaces
|March 7, 2025
PubMed
Summary

A new three-terminal nano-ionic solid electrolyte FET (SE-FET) enhances reservoir computing for edge systems. This technology improves temporal data processing, reduces costs, and achieves high accuracy in handwritten digit classification and chaotic time-series forecasting.

Keywords:
classificationedge systemsforecastingphysical reservoir computingsolid electrolyte FETtemporal data

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Area of Science:

  • Materials Science and Engineering
  • Computer Science and Engineering
  • Artificial Intelligence

Background:

  • Physical dynamic reservoirs are effective for edge systems, processing temporal data with low training costs via in-memory computation.
  • Limitations of two-terminal memristor reservoirs include restricted temporal input duration and high device counts for forecasting, increasing complexity and cost.
  • Existing systems struggle with long-term forecasting, often requiring feedback loops that add complexity.

Purpose of the Study:

  • To develop an efficient reservoir computing system using a three-terminal nano-ionic solid electrolyte field-effect transistor (SE-FET).
  • To overcome the short-term memory limitations of traditional two-terminal devices for extended temporal input processing.
  • To enhance reservoir computing efficiency and reduce hardware/training costs for edge applications like classification and forecasting.

Main Methods:

  • Utilized a three-terminal nano-ionic solid electrolyte FET (SE-FET) where drain current is regulated by gate and drain voltages.
  • Implemented a separate control terminal for read/write operations, simplifying design and enhancing reservoir efficiency.
  • Tested the SE-FET reservoir for handwritten digit classification and chaotic time-series forecasting tasks.

Main Results:

  • Achieved 95.41% accuracy in handwritten digit classification with a longer mask length, using 51% fewer reservoir outputs per sample.
  • Demonstrated efficient long-term forecasting of 50 time steps using only four SE-FET devices without feedback, achieving a low root-mean-square error of 0.06.
  • The SE-FET approach significantly reduces hardware and training costs without compromising classification accuracy.

Conclusions:

  • The three-terminal SE-FET offers an efficient and cost-effective solution for reservoir computing in edge systems.
  • Extended short-term memory and simplified design enable superior performance in temporal data processing, classification, and long-term forecasting.
  • This technology represents a significant advancement over traditional two-terminal devices, paving the way for more complex edge AI applications.