최신현 교수

Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor's non-reliability issues ha

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

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high

Neuromorphic computing, an alternative for von Neumann architecture, requires synapse devices where the data can be stored and computed in the same place. The three-terminal synapse device is attractive for neuromorphic computing due to its high stability and controllability. However, high nonlinearity on weight update, low dynamic range, and incompatibility with conventional CMOS systems have been reported as obstacles for large-scale crossbar arrays. Here, we propose the CMOS compatible gate i

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− x memristors, we experimentally demo

Abstract The sensory nervous system in animals enables the perception of external stimuli. Developing an artificial sensory nervous system has been widely conducted to realize neuro-inspired robots capable of effectively responding to external stimuli. However, it remains challenging to develop artificial sensory nervous systems that possess sophisticated biological functions, such as habituation and sensitization, enabling efficient responses without bulky peripheral circuitry. Here, we introdu

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