Jang-Yeon Kwon

Reservoir computing (RC) offers a promising route to real‐time temporal information processing and is widely regarded as an energy‐efficient alternative to conventional von Neumann architectures. Paradoxically, many hardware RC systems have yet to demonstrate clear advantages in both array‐level energy efficiency and processing speed, despite these being among the field's central motivations. Furthermore, their practical viability is limited by structural complexity and insufficient robustness. Here, we report a reconfigurable, dynamic random‐access memory (DRAM)‐like physical RC platform based on an oxide channel two‐transistor (2T) cell, providing a practical route toward low‐energy, fast, robust, and structurally simple physical RC. Within a single device footprint and fabrication flow, the same 2T cell can be reconfigured to volatile reservoir dynamics or quasi‐non‐volatile readout memory. Its operating mode is selected by the hold bias, enabling electrical reconfiguration with tunable relaxation dynamics. This compact strategy enables spatiotemporal programmability while preserving process simplicity, as validated by motion‐detection simulations. Owing to multi‐V _hold spatiotemporal encoding, the proposed RC architecture achieves a 3.98‐fold reduction in device‐array‐level read energy and requires 8.00‐fold fewer trainable synaptic weights than the ANN baseline. These results position oxide 2T memory as a strong candidate for next‐generation non‐von‐Neumann artificial intelligence (AI) hardware.

Organic light-emitting diodes on silicon have emerged as a key technology for ultrahigh-resolution near-to-eye (NTE) displays by offering fast response, wide color gamut, and complementary metal–oxide–semiconductor-compatible processing for >2000 pixels per inch (PPI). However, despite achieving high PPI levels, existing NTE displays suffer from limited battery life and prohibitive costs. While oxide semiconductor thin-film transistors (TFTs) are promising alternatives, most studies have focused on low thermal budgets for glass substrates and relatively long channel lengths. Therefore, we experimentally investigated the impact of high annealing temperatures on 1 μm short-channel InSnZnO (ITZO) self-aligned top-gate (SATG) TFTs. A high field-effect mobility of 14.5 cm2/V s is achieved at 450 °C, while a minimum drain-induced barrier lowering of 34.9 mV/V was observed at 850 °C. Thin-film analyses and contact resistance measurements revealed the underlying reasons for the stable operation of high-temperature-annealed ITZO SATG TFTs with 1 μm channel lengths.

The substantial power consumption of traditional computing architectures, arising from the physical separation of memory and processing units, has motivated the exploration of neuromorphic systems that emulate the remarkable energy efficiency of the human brain. However, artificial neural networks implemented on neuromorphic devices still require billions of weight updates, and conventional devices typically consume power in the milliwatt range per switching event, posing a significant challenge even for neuromorphic systems. In this study, a synapse‐like memristive device utilizing a tyrosine‐rich peptide is presented as the resistive switching layer. By emulating proton‐mediated signaling of biological synapses, the device leverages proton–electron dual‐carrier transport enabled by the redox‐active properties of tyrosine to realize ultralow‐power resistive switching. Proton modulation is implemented through two methods: i) exposure to external humidity and ii) electrically driven injection using a PdH _x proton reservoir layer. Comparative analysis reveals that the electrically driven approach achieves an ultralow switching power of 215 pW—≈2500 times lower than that of the intrinsic device—primarily owing to the sustained low off‐current during proton injection. These results demonstrate a promising strategy for developing highly energy‐efficient, synapse‐like memristive devices through precise control of proton dynamics.

We present an AI-integrated molecular communication link validated on a benchtop nanomachine testbed representative of subdermal implants. The system employs an indium-gallium-zinc-oxide electrolyte-gated FET (IGZO-EGFET) functionalized with glucose oxidase as a biocompatible receiver, a microfluidic channel with a syringe-pump transmitter using on-off keying (OOK), and a machine-intelligence pipeline that addresses model mismatch and hardware non-idealities. The pipeline integrates: (i) a modular universal decoder robust to vibration-induced noise, chemical delay, and single-tap intersymbol interference; (ii) a lightweight pilot-only synchronizer that estimates symbol intervals; and (iii) a virtual-response generator that augments data and scales symbol duration. Experiments across multiple chips and sessions demonstrate end-to-end chemical text transmission with consistent error-rate reductions compared to naive thresholding and standard neural baselines. By coupling biocompatible hardware with learning-based detection and generative augmentation, this work establishes a practical route toward AI-native nanomachine networks and higher rate molecular links, while providing a system blueprint adaptable to other biochemical modalities.

Atomic layer deposition-grown beryllium oxide (BeO) is gaining attention as a dielectric material that can minimize device power consumption because of its high dielectric constant, high thermal conductivity, and low leakage current enabled by its wide bandgap energy. In this study, the impact of BeO dielectrics on InSnZnO (ITZO) thin-film transistors (TFTs) was investigated, revealing that adding a hafnium dioxide (HfO₂) layer can enhance electrical performance and bias stress reliability. Time-of-flight secondary-ion mass spectrometry and X-ray photoelectron spectroscopy confirmed that the single-BeO dielectric-based ITZO TFTs exhibited a low mobility of 27.6 cm²/V·s due to Be migration and demonstrated abnormal threshold voltage (V_TH) shifts under bias stress. Conversely, the HfO₂ 20 nm/BeO hetero-dielectric ITZO TFTs exhibited a high mobility of 76.6 cm²/V·s and enhanced abnormal V_TH shift characteristics. Therefore, these results demonstrate that our high-performance HfO₂/BeO hetero-dielectric-based ITZO TFTs could be utilized in back-end-of-line devices for monolithic three-dimensional memory technologies.