김민휘 교수

In flexible neuromorphic systems for realizing artificial intelligence, organic memristors are essential building blocks as artificial synapses to perform information processing and memory. Despite much effort to implement artificial neural networks (ANNs) using organic memristors, the reliability of these devices is inherently hampered by global ion transportation and arbitrary growth of conductive filaments (CFs). As a result, the performance of ANNs is restricted. Herein, a novel concept for

Abstract In this study, we propose an effective strategy for achieving the flexible one organic transistor–one organic memristor (1T–1R) synapse using the multifunctional organic memristor. The dynamics of the conductive nanofilament (CF) in a hydrophobic fluoropolymer medium is explored and a hydrophobic fluoropolymer-based organic memristor is developed. The flexible 1T–1R synapse can be fabricated using the solution process because the hydrophobic fluorinated polymer layer is produced on the

The decarbonization of buildings and communities has become a requirement for sustainability. In this regard, South Korea has set a target of net-zero-energy buildings, wherein all public buildings with a floor area exceeding 500 m2 must cover 80% of the primary energy consumption using renewable energy systems by 2030. This study investigated the improvement and maximization of energy from renewable energy systems through energy sharing between buildings by retrofitting an existing community. T

In this paper, we have investigated the effect of additional thin SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> layer on switching variability of SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> -based resistive memory (RRAM). We found that excessive LRS state generated in set operation results in large reset current and abrupt reset operation. The abrupt reset operation leads t

Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration

In recent days, more hardware-driven artificial intelligence system capable of brain-like low-energy consumption is gaining ever-increasing interest. The hardware-driven property lies in the low-power synaptic device and its array along with the area and energy-efficient neuron circuits. In this work, a spiking neural network (SNN) based on analog synaptic device of resistive-switching random access memory (RRAM) is constructed from the experimentally fabricated devices. Furthermore, the capabil

In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated. To implement the on-chip trainable neuromorphic system which utilizes large-scale hardware synapse units, a fast and reliable write scheme needs to be established. Based on the experimental results, it is confirmed that the gradual set and full reset operation is the most suitable operation scheme for fast programming due to the fundamental

Abstract In this work, nanoscale wedge-structured silicon nitride (SiN x )-based resistive-switching random-access memory with data non-volatility and conductance graduality has been designed, fabricated, and characterized for its application in the hardware neuromorphic system. The process integration with full Si-processing-compatibility for constructing the unique wedge structure by which the electrostatic effects in the synaptic device operations are maximized is demonstrated. The learning b

As the demand for bio‐inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one‐transistor‐one‐resistor (1T‐1R) and 0T‐1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 10 3 , achieved by integrating both an overshoot suppression layer (OSL) and an ultra‐thin AlN oxygen barrier layer (OBL) is also reported.

The demand for district heating and cooling systems in block units with a heat pump that utilizes various unused energy sources for energy supply has been increasing. This study investigated experimentally the ground source heat pump (GSHP) and sewage water source heat pump (SWSHP) facilities used in block cooling and heating networks. Then, a heat pump performance prediction model was derived for utilization in future designs. Operational data for heating and cooling energy supply from an exper

Due to increased grid problems caused by renewable energy systems being used to realize zero energy buildings and communities, the importance of energy sharing and self-sufficiency of renewable energy also increased. In this study, the energy performance of an energy-sharing community was investigated to improve its energy efficiency and renewable energy self-sufficiency. For a case study, a smart village was selected via detailed simulation. In this study, the thermal energy for cooling, heatin

In this study, we meticulously engineered an Al-doped hafnia-based ferroelectric tunneling junction (FTJ) with a metal–ferroelectric–silicon (MFS) structure. We conducted a thorough analysis of its memory characteristics, revealing a substantial remnant polarization of 24.17 μC/cm2, a noteworthy tunneling electroresistance value of 265, exceptional endurance with 106 operational cycles, and robust retention (>104 s), thereby demonstrating the viability of the FTJ as a nonvolatile memory device.