Spin–Charge Conversion-Based Artificial Synaptic Device for Neuromorphic Computing
Source abstract
Abstract
A synaptic function is demonstrated using spin–charge conversion in a Rashba system. In an asymmetric quantum well channel, fast-moving charges induce a Rashba effective magnetic field, which separates spin-up and spin-down potentials. The ferromagnet detects these spin-dependent potentials, corresponding to the spin information on the channel. The multiple ferromagnetic electrodes, each with different switching fields, probe their respective spin potentials, and the output terminal reads the superposition of the detected potentials, thereby realizing multiple voltage states. These multiple states are systematically modulated and changed to any desired state directly, enabling both the potentiation and depression of synaptic behavior. In this memristive function device, both charge-to-spin and spin-to-charge conversions are demonstrated in a single device, consistent with the reciprocal relation. Additionally, neuromorphic pattern recognition is clearly demonstrated by controlling the V max / V min ratio and offset resistance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
KU-KIST Graduate School of Converging Science and Technology
Korea University
Center for Semiconductor Technology
Korea Institute of Science and Technology
Center for Quantum Technology
Department of Electrical Engineering
Division of Nanoscience & Technology, KIST School
University of Science and Technology
Funding
KU-KIST Graduate School of Converging Science and Technology · National Research Council of Science and Technology · Korea Institute of Science and Technology
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