Jeong-Ho Yoon
Sungkyunkwan University · 工学
研究室紹介
Professor Jeong-Ho Yoon's research lab specializes in advanced resistive switching memory devices and artificial neuromorphic systems, focusing on the development of next-generation ReRAM (Resistive Random Access Memory) with superior scalability, low power consumption, and high reliability. The lab pioneers electroforming-free, self-rectifying, and nanoscale memristor architectures—particularly using novel mobile species like Ru and tailored oxide heterostructures (e.g., HfO₂/Ta₂O₅)—to enable high-density memory and brain-inspired computing. A key research direction involves mimicking biological nociceptors through diffusive memristors for real-time sensory alarm systems, bridging neuromorphic engineering with biomedical and robotic applications. The lab emphasizes fundamental device physics, interface engineering, and circuit integration for emerging applications in AI hardware and intelligent robotics.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the "no adaptation" and "sensitization" phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with cr
The development of a resistance switching (RS) memory cell that contains rectification functionality in itself, highly reproducible RS performance, and electroforming‐free characteristics is an impending task for the development of resistance switching random access memory. In this work, a two‐layered dielectric structure consisting of HfO 2 and Ta 2 O 5 layers, which are in contact with the TiN and Pt electrode, is presented for achieving these tasks simultaneously in one sample configuration.
Limiting the location where electron injection occurs at the cathode interface to a narrower region is the key factor for achieving a highly improved RS performance, which can be achieved by including Ru Nanodots. The development of a memory cell structure truly at the nanoscale with such a limiting factor for the electric-field distribution can solve the non-uniformity issue of future ReRAM.
Pt/Ta2 O5 /HfO2- x /Ti resistive switching memory with a new circuit design is presented as a feasible candidate to succeed multilevel-cell (MLC) NAND flash memory. This device has the following characteristics: 3 bit MLC, electroforming-free, self-rectifying, much higher cell resistance than interconnection wire resistance, low voltage operation, low power consumption, long-term reliability, and only an electronic switching mechanism, without an ionic-motion-related mechanism.
1S1R (1 selector and 1 memristor) is a laterally scalable and vertically stackable scheme that can lead to the ultimate memristor density for either memory or neural network applications. In such a scheme, the memristor device needs to be truly electroforming‐free and operated at both low currents and low voltages in order to be compatible with a two‐terminal selector. In this work, a new type of memristor with a preconditioned tunneling conductive path is developed to achieve the required perfo
Abstract The switching parameters and device performance of memristors are predominately determined by their mobile species and matrix materials. Devices with oxygen or oxygen vacancies as the mobile species usually exhibit a great retention but also need a relatively high switching current (e.g., >30 µA), while devices with Ag or Cu as cation mobile species do not require a high switching current but usually show a poor retention. Here, Ru is studied as a new type of mobile species for memri
Abstract Conductive bridge random access memory (CBRAM) has been regarded as a promising candidate for the next‐generation nonvolatile memory technology. Even with the great performance of CBRAM, the global generation and overinjection of cations after much repetitive switching cannot be prevented. The overinjection of cations into an electrolyte layer causes high‐resistance‐state resistance ( R HRS ) degradation, on/off ratio reduction, and eventual switching failure. It also degrades the switc
Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation,
To replace or succeed the present NAND flash memory, resistive switching random access memory (ReRAM) should be implemented in the vertical-type crossbar array configuration. The ReRAM cell must have a highly reproducible resistive switching (RS) performance and an electroforming-free, self-rectifying, low-power-consumption, multilevel-switching, and easy fabrication process with a deep sub-μm(2) cell area. In this work, a Pt/Ta2O5/HfO2-x/TiN RS memory cell fabricated in the form of a vertical-t
A biological receptor serves as sensory transduction from an external stimulus to an electrical signal. It allows humans to better match the environment by filtering out repetitive innocuous information and recognize potentially damaging stimuli through key features, including adaptive and maladaptive behaviors. Herein, for the first time, the authors develop substantial artificial receptors involving both adaptive and maladaptive behaviors using diffusive memristor. Metal-oxide nanorods (NR) as
The Pt/TiO<sub>2</sub>/HfO<sub>2−x</sub>/TiN resistive switching memory structure showed self-rectifying resistive switching behavior with unprecedented unique <italic>I</italic>–<italic>V</italic> curves named “self-current saturation”, which can give an extremely uniform variation of the low resistance state.
Ru nano-dots were embedded in a Pt/TiO2/Pt resistive switching cell to improve the uniformity of the switching parameters. The TiO2 film grown on the Ru nano-dots had a rutile structure locally whereas other parts of the TiO2 film had an anatase structure. The rutile-structured TiO2 formed conducting filaments easily and their rupture was much more uniform than the randomized ones in anatase TiO2. This largely improved the resistance uniformity at the reading voltage during the repeated resistan
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. Und
The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen- or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-to-device variations. In addition, the hard-to-control redox reaction during repeated switching causes random fluctuations or
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 sele