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1. Reconfigurable Physical Reservoir in GaN/α-In2Se3 HEMTs Enabled by Out-of-Plane Local Polarization of Ferroelectric 2D Layer

   https://doi.org/10.1021/acsnano.3c00187  

   Yang, Jeong Yong; Park, Minseong; Yeom, Min Jae; Baek, Yongmin; Yoon, Seok Chan; Jeong, Yeong Je; Oh, Seung Yoon; Lee, Kyusang; Yoo, Geonwook (April 2023, ACS Nano)

   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 α-In2Se3 ferroelectric semiconductor. The van der Waals heterostructure of GaN/α-In2Se3 provides a potential to achieve high-frequency operation driven by a ferroelectrically coupled two-dimensional electron gas (2DEG). Moreover, the semiconducting α-In2Se3 features a steep subthreshold slope with a high ON/OFF ratio (∼1010). The self-aligned α-In2Se3 layer with the gate electrode suppresses the in-plane polarization while promoting the out-of-plane (OOP) polarization of α-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/α-In2Se3 HEMT can provide a viable pathway toward ultrafast neuromorphic computing. 

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2. Synaptic plasticity emulation by natural biomaterial honey-CNT-based memristors

   https://doi.org/10.1063/5.0174426  

   Templin, Zoe; Tanim, Md Mehedi; Zhao, Feng (December 2023, Applied Physics Letters)

   Artificial synaptic devices made from natural biomaterials capable of emulating functions of biological synapses, such as synaptic plasticity and memory functions, are desirable for the construction of brain-inspired neuromorphic computing systems. The metal/dielectric/metal device structure is analogous to the pre-synapse/synaptic cleft/post-synapse structure of the biological neuron, while using natural biomaterials promotes ecologically friendly, sustainable, renewable, and low-cost electronic devices. In this work, artificial synaptic devices made from honey mixed with carbon nanotubes, honey-carbon nanotube (CNT) memristors, were investigated. The devices emulated spike-timing-dependent plasticity, with synaptic weight as high as 500%, and demonstrated a paired-pulse facilitation gain of 800%, which is the largest value ever reported. 206-level long-term potentiation (LTP) and long-term depression (LTD) were demonstrated. A conduction model was applied to explain the filament formation and dissolution in the honey-CNT film, and compared to the LTP/LTD mechanism in biological synapses. In addition, the short-term and long-term memory behaviors were clearly demonstrated by an array of 5 × 5 devices. This study shows that the honey-CNT memristor is a promising artificial synaptic device technology for applications in sustainable neuromorphic computing. 

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3. Record >10 MV/cm mesa breakdown fields in Al _0.85 Ga _0.15 N/Al _0.6 Ga _0.4 N high electron mobility transistors on native AlN substrates

   https://doi.org/10.1063/5.0083966  

   Khachariya, Dolar; Mita, Seiji; Reddy, Pramod; Dangi, Saroj; Dycus, J. Houston; Bagheri, Pegah; Breckenridge, M. Hayden; Sengupta, Rohan; Rathkanthiwar, Shashwat; Kirste, Ronny; et al (April 2022, Applied Physics Letters)

   The ultra-wide bandgap of Al-rich AlGaN is expected to support a significantly larger breakdown field compared to GaN, but the reported performance thus far has been limited by the use of foreign substrates. In this Letter, the material and electrical properties of Al 0.85 Ga 0.15 N/Al 0.6 Ga 0.4 N high electron mobility transistors (HEMT) grown on a 2-in. single crystal AlN substrate are investigated, and it is demonstrated that native AlN substrates unlock the potential for Al-rich AlGaN to sustain large fields in such devices. We further study how Ohmic contacts made directly to a Si-doped channel layer reduce the knee voltage and increase the output current density. High-quality AlGaN growth is confirmed via scanning transmission electron microscopy, which also reveals the absence of metal penetration at the Ohmic contact interface and is in contrast to established GaN HEMT technology. Two-terminal mesa breakdown characteristics with 1.3  μm separation possess a record-high breakdown field strength of ∼11.5 MV/cm for an undoped Al 0.6 Ga 0.4 N-channel layer. The breakdown voltages for three-terminal devices measured with gate-drain distances of 4 and 9  μm are 850 and 1500 V, respectively. 

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4. Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems

   https://doi.org/10.1088/1361-6463/ac585b  

   Sueoka, Brandon; Zhao, Feng (March 2022, Journal of Physics D: Applied Physics)

   Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems. 

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5. 2.2 W/mm at 94 GHz in AlN/GaN/AlN High‐Electron‐Mobility Transistors on SiC

   https://doi.org/10.1002/pssa.202200774  

   Hickman, Austin; Chaudhuri, Reet; Li, Lei; Nomoto, Kazuki; Moser, Neil; Elliott, Michael; Guidry, Matthew; Shinohara, Keisuke; Hwang, James_C_M; Xing, Huili_Grace; et al (January 2023, physica status solidi (a))

   Aluminum nitride (AlN) offers novel potential for electronic integration and performance benefits for high‐power, millimeter‐wave amplification. Herein, load‐pull power performance at 30 and 94 GHz for AlN/GaN/AlN high‐electron‐mobility transistors (HEMTs) on silicon carbide (SiC) is reported. When tuned for peak power‐added efficiency (PAE), the reported AlN/GaN/AlN HEMT shows PAE of 25% and 15%, with associated output power () of 2.5 and 1.7 W mm⁻¹, at 30 and 94 GHz, respectively. At 94 GHz, the maximum generated is 2.2 W mm⁻¹, with associated PAE of 13%. 

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