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Updated: Sep 16, 2025

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A neuromorphic processor with on-chip learning for beyond-CMOS device integration.

Hugh Greatorex1,2, Ole Richter3,4, Michele Mastella5

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This study introduces a mixed-signal neuromorphic architecture for integrating emerging memory devices and on-chip learning. It bridges the gap between silicon-based systems and advanced materials for brain-inspired computing.

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computer Architecture

Background:

  • Emerging memory technologies offer potential for neuromorphic systems but face integration challenges.
  • A gap exists between material development and large-scale, functional neuromorphic system realization.
  • Selecting optimal devices and materials for specific functions and CMOS integration is crucial.

Purpose of the Study:

  • To present a mixed-signal neuromorphic architecture for exploring on-chip learning and novel device integration.
  • To serve as a platform bridging silicon-based neuromorphic computation and emerging devices.
  • To demonstrate the architecture's readiness for device integration through testing and simulation.

Main Methods:

  • Development of a mixed-signal neuromorphic architecture.
  • Integration of on-chip learning circuits and novel two- and three-terminal devices.
  • Comprehensive measurements and simulations to validate device integration readiness.

Main Results:

  • Demonstrated the architecture's capability to integrate emerging devices.
  • Validated the platform's suitability for testing bio-inspired learning algorithms.
  • Established a tangible link between brain-inspired computation and device research.

Conclusions:

  • The presented architecture is ready for integrating novel memory devices into neuromorphic systems.
  • The platform facilitates the practical testing of emerging devices with bio-inspired algorithms.
  • This work bridges the gap between advanced materials and functional neuromorphic electronic systems.