최신현 교수

Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the

Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state var

Resistive random access memory (RRAM) devices (e.g."memristors") are widely believed to be a promising candidate for future memory and logic applications. Although excellent performance has been reported, the nature of resistance switching is still under extensive debate. In this study, we perform systematic investigation of the resistance switching mechanism in a TaOx based RRAM through detailed noise analysis, and show that the resistance switching from high-resistance to low-resistance is acc

Conductive-bridging random access memory (CBRAM) has garnered attention as a building block of non-von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability. In this study, a highly reliable nanoporous-defective bot

Resistive switching devices (RRAMs) have been proposed a promising candidate for future memory and neuromorphic applications. Central to the successful application of these emerging devices is the understanding of the resistance switching and failure mechanism, and identification of key physical parameters that will enable continued device optimization. In this study, we report detailed retention analysis of a TaOx based RRAM at high temperatures and the development of a microscopic oxygen diffu

Memristive neuromorphic computing has emerged as a promising computing paradigm for the upcoming artificial intelligence era, offering low power consumption and high speed. However, its commercialization remains challenging due to reliability issues from stochastic ion movements. Here, we propose an innovative method to enhance the memristive uniformity and performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO_2-<i>x</i> memristors, we exp

The dissemination of edge devices drives new requirements for security primitives for privacy protection and chip authentication. Memristors are promising entropy sources for realizing hardware‐based security primitives due to their intrinsic randomness and stochastic properties. With the adoption of memristors among several technologies that meet essential requirements, the neural network physically unclonable function (NNPUF) is proposed, a novel PUF design that takes advantage of deep learnin

Memristors have attracted considerable attention as next-generation devices for logic and neuromorphic computing applications, owing to their high on/off current ratio, low power consumption, and high switching speed. Despite the various excellent characteristics of memristors, they suffer from unstable conductive filament-based switching when applied in real-world applications. To address this issue, the effects of Schottky barrier modulation on device performance, in terms of conduction and fa

Monolithic 3D integration of neuron and synapse devices is considered a promising solution for energy-efficient and compact neuromorphic hardware. However, achieving optimal performance in both training and inference remains challenging as these processes require different synapse devices with reliable endurance and long retention. Here, we introduce a decoupling strategy to separate training and inference using monolithically integrated neuromorphic hardware with layer-by-layer fabrication. Thi

Abstract Conductive bridge random‐access memory (CBRAM) are two terminal devices that offer excellent switching performance. In addition, CBRAM shows various switching modes, including volatile threshold switching (TS) and nonvolatile threshold switching (N‐TS). These properties expand its applications to memory, selector, biological synapses, and neurons. However, due to the uncontrollable behavior of stochastic switching between TS and N‐TS in CBRAM devices, a novel approach is needed to impro

Security Primitives In article number 2100111, Shinhyun Choi and co-workers propose a trainable security primitive integrated with deep neural network and memristor arrays. The entire system can achieve optimal performance for security metrics by training. In the cover image, the trained encryption system only allows access to confidential information through an authenticated code (denoted by a blue key) and secures the information from adversaries (denoted by red keys).

Implementations of artificial synapse and neuron are presented by combining the silicon nanowire ionsensitive field-effect transistor (ISFET) and the indium-gallium-zinc-oxide (IGZO) memristor. Chemical and electrical operations of synapse are emulated by the pH sensor operation of ISFET and by the long-term potentiation of IGZO memristor, respectively. The concentration of hydrogen ions in electrolyte is successfully transformed via the voltage-controlled oscillator (VCO)-based neuron into the

This talk will cover how to achieve reliable emerging memory devices and real-time demonstration of ReRAMbased hardware platform.