Hee-Dong Kim

Resistive random-access memory (RRAM) has been widely explored with different resistive switching (RS) materials including oxides and nitrides, aiming for high-density and low-cost nonvolatile memory (NVM) solutions. This review focuses on the advancements in nitride-based RS materials for RRAM devices. The unique properties of nitride materials, including their reduced initial forming voltage and low dielectric strength, make them a promising choice for RRAM development. Nitride-based RRAM also addresses critical challenges, such as optimizing RS materials, reducing operating currents, enhancing thermal stability, and improving device reliability. These attributes position nitride-based RRAM as a strong contender for next-generation NVM technologies. Additionally, the potential of nitride materials in neuromorphic applications is explored, highlighting their ability to replicate synaptic behavior and enable energy-efficient computing architectures. Recent progress, challenges, and future directions in nitride-based materials are reviewed to provide valuable insights into their optimization and scalability. This study serves as a foundation for advancing electronic materials research, offering a pathway for the development of high-performance memory and neuromorphic systems involving nitride-based materials.

Flexible gas sensors for wearable electronics are often limited by performance degradation under mechanical deformation. This study presents a flexible nitrogen dioxide (NO₂) gas sensor based on a memristor heater (MH) structure using single-walled carbon nanotubes (SWCNTs) as the sensing layer. Hafnium dioxide (HfO₂) and indium gallium zinc oxide (IGZO) were compared as the active switching layer to evaluate their effects on sensing reliability under bending. The device operates through conductive filaments (CFs)-mediated resistive switching, enabling localized Joule heating at low voltage. Gas sensing was evaluated at 50 ppm NO₂ under bending angles of 0°, 30°, 60°, and 90°, and electrical stability was examined over up to 1,000 bending cycles. The IGZO-based sensor maintained more stable gas response and electrical characteristics and exhibited much lower crack density at 90° bending. These results indicate that suppressing deformation-induced disruption of CFs pathways is essential for reliable flexible MH gas sensors.

Research into memristor‐based artificial nociceptors has rapidly progressed in biomimetics. However, most studies have focused on single‐stimulus nociceptive function, leaving polymodality, particularly temperature‐dependent threshold modulation, significantly underexplored. Here, we propose a volatile TiN/TiO _x /ZnO/TiO _x /ITO (TZT) memristor designed to realize comprehensive polymodal nociceptive functionalities. Crucially, our device exhibits temperature‐dependent conductance, enabling the emulation of pain threshold modulation. Based on this property, we demonstrate the polymodal features in fundamental nociceptor behaviors, including threshold response, no‐adaptation, sensitization, and relaxation. We further replicated these behaviors at the system level by integrating the memristor with a force‐sensitive resistor and a microheater. The resulting polymodal nociceptor system successfully generates appropriate nociceptive signals in response to both mechanical and thermal stimuli. These findings, which move beyond single‐stimulus confinement, highlight the potential of our device as an advanced artificial nociceptor capable of providing a broader spectrum of pain responses in biomimetic platforms.

In this work, an HfO ₂ ‐based memristor exhibiting bimodal switching, wherein the device's conductance is modulated not only by the input stimulus but also by the polarity of the read voltage, is introduced. Uniquely, this device demonstrates reliable short‐term memory (STM)‐like behavior and supports 16 well‐separated conductance states through 4‐bit pulsed inputs. Remarkably, under the same input conditions, reversing the polarity of the read voltage results in 16 more different conductance states, thereby doubling the number of levels that can be distinguished per cell. Employing the proposed device, a reservoir computing (RC) system, which takes advantage of this rich representational capability, is created. The system achieves a high classification accuracy of 98.81% on the MNIST dataset. These results show how powerful memristor‐based architectures can be and how this device could be a compact and energy‐efficient platform for the next generation of neuromorphic computing.

Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH₃, and NO₂. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems.