Jeong-Min Park

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.

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.

We developed a two-transistor, zero-capacitor (2T0C) gain-cell memory featuring a self-aligned top-gate-structured thin-film transistor (TFT) for the first time. The proposed indium tin zinc oxide (ITZO) channel-incorporated architecture was specifically engineered to minimize parasitic capacitance for achieving long-retention 2T0C memory operations. A typical 2T0C structure features five types of parasitic capacitances; however, the proposed SATG design effectively used an essential gate insulator capacitance (C _OX) and reduced four nonessential capacitances (C _WBL-WWL, C _WWL-SN, C _RWL-SN, and C _RBL-SN) to virtually zero. The ITZO-based 2T0C gain-cell memory achieved a retention time >10,000 s owing to the extremely low off-current (2.33 × 10^-18 A/μm), superior positive-bias stability (0.71 V), and high saturation mobility [17.52 cm²/(V s)] of the optimized TFT structure. Our proposed memory with long retention and high endurance is a promising solution for next-generation 3D-integrated stacked dynamic random-access memories and defines a new structural standard for future memory architectures.

Artificial intelligence has become an essential part of the daily lives and has revolutionized various sectors, including healthcare, finance, transportation, and entertainment. With a substantial increase in processed data, neuromorphic devices that replicate the operation of the human brain have been emphasized owing to their superior efficiency. Typical neuromorphic devices focus on constructing synapse‐like structures. However, biological synapses have more complex mechanisms for efficient data processing. One of the most prominent mechanisms is proton activation, which forms an ion concentration gradient prior to the transmission of neurotransmitters and plays a key role in efficient computation. In this study, proton‐mediated signaling at biological synapses is successfully replicated by fabricating a proton‐modulated memristor device using a tyrosine‐rich peptide film. The ionic input of the memristor is controlled by applying a voltage to proton‐permeable PdH_x contacts in a hydrogen atmosphere, thus successfully adjusting the resistive switching behavior. Remarkable improvements in resistive switching and computing performance are observed through proton injection, analogous to “proton‐mediated signaling” at the actual synapse. It is believed that this study proposes a new paradigm for designing biorealistic devices and provides inspiration for precisely controllable ion‐based neuromorphic devices.

Recently, the growing demand for amorphous oxide semiconductor thin-film transistors (AOS TFTs) with high mobility and good stability to implement ultrahigh-resolution displays has made tracking the role of hydrogen in oxide semiconductor films increasingly important. Hydrogen is an essential element that contributes significantly to the field effect mobility and bias stability characteristics of AOS TFTs. However, because hydrogen is the lightest atom and has high reactivity to metal and oxide materials, elucidating its impact on AOS thin films has been challenging. Therefore, in this study, we propose controlling the hydrogen quantities in amorphous InSnZnO (a-ITZO) thin films through thermal dehydrogenation to precisely reveal the hydrogen influences on the electrical characteristics of a-ITZO TFTs. The as-deposited device containing 15.69 × 10¹⁵ atoms/cm² of hydrogen exhibited a relatively low saturation mobility of 18.1 cm²/V·s and poor positive bias stress stability. However, depending on the extent of thermal dehydrogenation, not only did the hydrogen quantity and interface defect density (D_IT) decrease but also the conductivity and surface energy increased due to the rise in oxygen vacancies and hydroxyl groups in a-ITZO thin films. As a result, the a-ITZO TFT with a hydrogen amount of 4.828 × 10¹⁵ atoms/cm² showed that the saturation mobility improved up to 36.8 cm²/V·s, and positive bias stress stability was remarkably enhanced. Hence, we report the ability to manage the hydrogen quantity with thermal dehydrogenation and demonstrate that high-performance a-ITZO TFTs can be realized when an appropriate hydrogen concentration is achieved.