Hakcheon Jeong

ORCID: 0000-0003-1654-825X
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Research Areas
  • Advanced Memory and Neural Computing
  • Neuroscience and Neural Engineering
  • Neural Networks and Reservoir Computing
  • Photoreceptor and optogenetics research
  • Ferroelectric and Negative Capacitance Devices
  • Semiconductor materials and devices
  • Phase-change materials and chalcogenides
  • Perovskite Materials and Applications
  • Physical Unclonable Functions (PUFs) and Hardware Security
  • Integrated Circuits and Semiconductor Failure Analysis
  • Advanced Optical Sensing Technologies
  • Semiconductor materials and interfaces
  • Adversarial Robustness in Machine Learning
  • CCD and CMOS Imaging Sensors
  • Thin-Film Transistor Technologies
  • Ocular and Laser Science Research
  • Semiconductor Quantum Structures and Devices

Korea Advanced Institute of Science and Technology
2021-2025

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

10.1038/s41467-022-30539-6 article EN cc-by Nature Communications 2022-06-03

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

10.1038/s41467-022-34178-9 article EN cc-by Nature Communications 2022-10-28

Next-generation wireless communication such as sixth-generation (6G) and beyond is expected to require high-frequency, multifunctionality, power-efficiency systems. A III-V compound semiconductor a promising technology for high-frequency applications, Si complementary metal-oxide-semiconductor (CMOS) the never-beaten highly integrated digital circuits. To harness advantages of these two technologies, monolithic integration electronics beneficial, so that there have been everlasting efforts...

10.1021/acsnano.2c00334 article EN ACS Nano 2022-04-19

Memristors are two-terminal memory devices that can change the conductance state and store analog values. Thanks to their simple structure, suitability for high-density integration, non-volatile characteristics, memristors have been intensively studied as synapses in artificial neural network systems. Memristive networks theoretically better energy efficiency compared with conventional von Neumann computing processors. However, memristor crossbar array-based usually suffer from low accuracy...

10.1039/d3nh00121k article EN Nanoscale Horizons 2023-01-01

Memristive neuromorphic computing has emerged as a promising 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 enhance memristive uniformity performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO 2− x memristors, experimentally demonstrate reduced...

10.1126/sciadv.adm7221 article EN cc-by-nc Science Advances 2024-06-07

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, switching speed. Despite the various excellent characteristics of memristors, they suffer from unstable conductive filament-based when applied in real-world applications. To address this issue, effects Schottky barrier modulation on device performance, terms conduction failure mechanisms an Ag/WOx/p-Si...

10.1063/5.0131593 article EN cc-by Journal of Applied Physics 2023-02-15

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

10.1002/admi.202300975 article EN cc-by Advanced Materials Interfaces 2024-01-22

In this work, single photon avalanche diodes (SPADs) were fabricated using the standard 180 nm complementary metal-oxide semiconductor process. Their small size of 15–16 μ m and low operating voltage made it possible to easily integrate them with readout circuits for compact on-chip sensors, particularly those used in radiation sensor network a nuclear plant. Four architectures proposed SPADs, shallow trench isolation (STI) guard ring different depletion regions designed demonstrate main...

10.1016/j.net.2024.03.006 article EN cc-by-nc-nd Nuclear Engineering and Technology 2024-03-05

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

10.1002/aisy.202100111 article EN cc-by Advanced Intelligent Systems 2021-10-07

Abstract The emergence of technologies such as Artificial Intelligence (AI) and the Internet Things (IoT) has ushered in era big data. demand for low‐power hardware systems efficient algorithms become more imperative. In this study, an ultra‐low‐power dynamic memtransistor based on charge storage junction Field‐Effect Transistor (FET) with a step‐wise potential barrier is developed. A simple yet device structure allows analog programming spontaneous relaxation. demonstrated fast speed (tens...

10.1002/aelm.202300904 article EN Advanced Electronic Materials 2024-05-08

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

10.1002/aisy.202170074 article EN cc-by-nc Advanced Intelligent Systems 2021-11-01
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