Publications by authors named "Chang-Hyeon Han"
Analog reservoir computing (ARC) systems have attracted attention owing to their efficiency in processing temporal information. However, the distinct functionalities of the system components pose challenges for hardware implementation. Herein, we report a fully integrated ARC system that leverages material versatility of the ferroelectric-to-mixed phase boundary (MPB) hafnium zirconium oxides integrated onto indium-gallium-zinc oxide thin-film transistors (TFTs).
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- Artificial neurons and synapses are essential for building efficient spiking neural networks (SNNs), but their unique needs pose challenges in creating energy-efficient hardware.
- This study introduces an all-ferroelectric SNN system that utilizes a new double-gate morphotropic phase boundary thin-film transistor (DG MPBTFT) for leaky integrate-and-fire (LIF) neurons, improving space and energy efficiency by removing the need for capacitors and reset circuits.
- The integration of materials and devices led to an impressive classification accuracy of 94.9%, showcasing the potential of DG MPBTFT-based LIF neurons for enhancing neuromorphic computing capabilities.