Jung Ho Yoon’s research while affiliated with Sungkyunkwan University and other places

The advent of the Internet of Things (IoT) has led to an exponential growth in data generated from sensors. Consequently, a time- and energy-efficient method for processing complex and unstructured...

Flexible memristors are promising candidates for multifunctional neuromorphic computing applications, overcoming the limitations of conventional computing devices. However, unpredictable switching behavior and poor mechanical stability in conventional memristors present significant challenges to achieving device reliability. Here, a reliable and flexible memristor using zirconium‐oxo cluster (Zr6O4OH4(OMc)12) as the resistive switching layer is demonstrated. The optimization of the structural rigidity of the hybrid oxo‐cluster network by thermal polymerization allows the precise formation of dispersed conductive cluster networks, enhancing the repeatability of the resistive switching with mechanical flexibility. The optimized memristor exhibits endurance of ∼10⁴ cycles and stable memory retention performance up to 10⁴ s, maintaining a high ION/IOFF ratio of 10⁴ under a bending radius of 2.5 mm. Moreover, the device achieves a pattern recognition accuracy of 97.44%, enabled by highly symmetric analog switching with multilevel conductance states. These results highlight that hybrid metal‐oxo clusters can provide novel material design principles for flexible and reliable neuromorphic applications, contributing to the development of artificial neural networks.

Memristors exhibit changes in internal resistance in response to external voltage, introducing new functionalities to electronic devices. This enables diverse applications in non‐volatile memory, neuromorphic devices, sensors, and computing systems, highlighting their growing importance in electronics. These applications leverage various mechanisms underlying memristors. Therefore, understanding these mechanisms and discovering new memristive mechanisms are essential for overcoming implementation challenges and developing emerging applications. Here, a new type of memristor is introduced comprising an Ag/Ag:Cu‐islands/HfO2/Pt structure, characterized by a hybrid mechanism and its potential for multifunctional applications. The memristor combines the metallic filament (Ag/Cu alloy) of the electrochemical metallization (ECM) mechanism with the oxygen vacancy filament of the valence change memory (VCM) mechanism, achieving both the high on/off ratio of ECM and the analog characteristics of VCM with enhanced reliability. Both resistive and threshold switching characteristics are shown by controlling the compliance current, making the device applicable to artificial synapses and neurons. Notably, this device exhibits heat‐responsive nociceptor characteristics, positioning it as a promising candidate for next‐generation neuromorphic devices.

A self-rectifying ferroelectric tunnel junction that employs a HfO 2 /ZrO 2 /HfO 2 superlattice (HZH SL) combined with Al 2 O 3 and TiO 2 layers is proposed. The 6 nm-thick HZH SL effectively suppresses the formation of...

Inspired by the functions of biological neural networks, volatile memristors are essential for implementing neuromorphic computing. These devices enable large-scale and energy-efficient data processing by emulating neural functionalities through dynamic resistance changes. The threshold switching characteristics of volatile memristors, which are driven by various mechanisms in materials ranging from oxides to chalcogenides, make them versatile and suitable for neuromorphic computing systems. Understanding these mechanisms and selecting appropriate devices for specific applications are crucial for optimizing the performance. However, the existing literature lacks a comprehensive review of switching mechanisms, their compatibility with different applications, and a deeper exploration of the spatiotemporal processing capabilities and inherent stochasticity of volatile memristors. This review begins with a detailed analysis of the operational principles and material characteristics of volatile memristors. Their diverse applications are then explored, emphasizing their role in crossbar arrays, artificial receptors, and neurons. Furthermore, the potential of volatile memristors in artificial inference systems and reservoir computing is discussed, due to their spatiotemporal processing capabilities. Hardware security applications and probabilistic computing are also examined, where the inherent stochasticity of the devices can improve the system robustness and adaptability. To conclude, the suitability of different switching mechanisms for various applications is evaluated, and future perspectives for the development and implementation of volatile memristors are presented. This review aims to fill the gaps in existing research and highlight the potential of volatile memristors to drive innovation in neuromorphic computing, paving the way for more efficient and powerful computational paradigms.

HfO 2 shows promise for emerging ferroelectric and resistive switching (RS) memory devices owing to its excellent electrical properties and compatibility with complementary metal oxide semiconductor technology based on mature fabrication processes such as atomic layer deposition. Oxygen vacancy (V o ), which is the most frequently observed intrinsic defect in HfO 2 -based films, determines the physical/electrical properties and device performance. V o influences the polymorphism and the resulting ferroelectric properties of HfO 2 . Moreover, the switching speed and endurance of ferroelectric memories are strongly correlated to the V o concentration and redistribution. They also strongly influence the device-to-device and cycle-to-cycle variability of integrated circuits based on ferroelectric memories. The concentration, migration, and agglomeration of V o form the main mechanism behind the RS behavior observed in HfO 2 , suggesting that the device performance and reliability in terms of the operating voltage, switching speed, on/off ratio, analog conductance modulation, endurance, and retention are sensitive to V o . Therefore, the mechanism of V o formation and its effects on the chemical, physical, and electrical properties in ferroelectric and RS HfO 2 should be understood. This study comprehensively reviews the literature on V o in HfO 2 from the formation and influencing mechanism to material properties and device performance. This review contributes to the synergetic advances of current knowledge and technology in emerging HfO 2 -based semiconductor devices. Graphical Abstract

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real‐time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal‐processing functions such as selective adaption in receptors, leaky integrate‐and‐fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by “artificial receptors,” encoded into spike signals via “artificial neurons,” and integrated and stored through “artificial synapses.” The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high‐performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real‐time detection and processing of environmental information. This review explores the recent advances in memristor‐based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in‐depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor‐based artificial sensory systems are discussed.