Reconfigurable Physical Reservoir in GaN/?-In2Se3 HEMTs Enabled by Out-of-Plane Local Polarization of Ferroelectric 2D Layer
- Authors
- Yang, Jeong Yong; Park, Minseong; Yeom, Min Jae; Baek, Yongmin; Yoon, Seok Chan; Jeong, Yeong Je; Oh, Seung Yoon; Lee, Kyusang; Yoo, Geonwook
- Issue Date
- Apr-2023
- Publisher
- AMER CHEMICAL SOC
- Keywords
- GaN; ?-In2Se3 heterostructure; ferroelectric semiconductor; polarization; self-aligned structure; reservoir computing
- Citation
- ACS NANO, v.17, no.8, pp 7695 - 7704
- Pages
- 10
- Journal Title
- ACS NANO
- Volume
- 17
- Number
- 8
- Start Page
- 7695
- End Page
- 7704
- URI
- https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43884
- DOI
- 10.1021/acsnano.3c00187
- ISSN
- 1936-0851
1936-086X
- Abstract
- Significant effort for demonstrating a gallium nitride (GaN)-based ferroelectric metal-oxide-semiconductor (MOS)-high-electron-mobility transistor (HEMT) for reconfigurable operation via simple pulse operation has been hindered by the lack of suitable materials, gate structures, and intrinsic depolarization effects. In this study, we have demonstrated artificial synapses using a GaN-based MOS-HEMT integrated with an alpha-In2Se3 ferroelectric semiconductor. The van der Waals heterostructure of GaN/alpha-In2Se3 provides a potential to achieve high-frequency operation driven by a ferroelectrically coupled two-dimensional electron gas (2DEG). Moreover, the semiconducting alpha-In2Se3 features a steep subthreshold slope with a high ON/OFF ratio (similar to 1010). The self-aligned alpha-In2Se3 layer with the gate electrode suppresses the in-plane polarization while promoting the out-of-plane (OOP) polarization of alpha-In2Se3, resulting in a steep subthreshold slope (10 mV/dec) and creating a large hysteresis (2 V). Furthermore, based on the short-term plasticity (STP) characteristics of the fabricated ferroelectric HEMT, we demonstrated reservoir computing (RC) for image classification. We believe that the ferroelectric GaN/alpha-In2Se3 HEMT can provide a viable pathway toward ultrafast neuromorphic computing.
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