Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Jiyong Woo"'
Autor:
Seokjae Lim, Jiyong Woo
Publikováno v:
AIP Advances, Vol 14, Iss 7, Pp 075225-075225-6 (2024)
We demonstrate how to improve the turn-off speed of Ag-based volatile atomic switches with an Al2O3 electrolyte by understanding the origin of filament instability. Under the current sweep mode, our findings reveal that the formation of Ag–Te bondi
Externí odkaz:
https://doaj.org/article/abc190a52dd7462dbbd2793c84e14f1a
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-7 (2024)
Abstract Synaptic transistors (STs) with a gate/electrolyte/channel stack, where mobile ions are electrically driven across the solid electrolyte, have been considered as analog weight elements for neuromorphic computing. The current (ID) between the
Externí odkaz:
https://doaj.org/article/ea70c19dbcd9447793652193dd1bd56d
Publikováno v:
Nano Convergence, Vol 11, Iss 1, Pp 1-24 (2024)
Abstract Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs ge
Externí odkaz:
https://doaj.org/article/138be585c4b34ab4843fdedcd4bd9cea
Autor:
Tae Jun Yang, Jung Rae Cho, Hyunkyu Lee, Hee Jun Lee, Seung Joo Myoung, Da Yeon Lee, Sung-Jin Choi, Jong-Ho Bae, Dong Myong Kim, Changwook Kim, Jiyong Woo, Dae Hwan Kim
Publikováno v:
IEEE Access, Vol 12, Pp 28531-28537 (2024)
Obtaining symmetrical and highly linear synapse weight update characteristics of analog resistive switching devices is critical for attaining high performance and energy efficiency of the neural network system. In this work, based on the two-terminal
Externí odkaz:
https://doaj.org/article/83132ecb329e4de7a490a76eceb416e7
Autor:
Seonuk Jeon, Heebum Kang, Hyunjeong Kwak, Kyungmi Noh, Seungkun Kim, Nayeon Kim, Hyun Wook Kim, Eunryeong Hong, Seyoung Kim, Jiyong Woo
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-7 (2023)
Abstract The multilevel current states of synaptic devices in artificial neural networks enable next-generation computing to perform cognitive functions in an energy-efficient manner. Moreover, considering large-scale synaptic arrays, multiple states
Externí odkaz:
https://doaj.org/article/9bf5e1c30916423e96a03aa91d3f24ff
Autor:
Hyeonsik Choi, Hyun Wook Kim, Eunryeong Hong, Nayeon Kim, Seonuk Jeon, Yunsur Kim, Jiyong Woo
Publikováno v:
AIP Advances, Vol 14, Iss 1, Pp 015042-015042-7 (2024)
This study shows how the threshold switching (TS) characteristics of a NbOx layer with noninert W electrodes can be improved by introducing an oxide barrier. The ∼10-nm-thick NbOx layer exhibits TS, which is known to originate from NbO2, after elec
Externí odkaz:
https://doaj.org/article/5c43c27379a54128b0ccc262ad43c95e
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-6 (2023)
Abstract Three-terminal (3T) structured electrochemical random access memory (ECRAM) has been proposed as a synaptic device based on improved synaptic characteristics. However, the proposed 3T ECRAM has a larger area requirement than 2T synaptic devi
Externí odkaz:
https://doaj.org/article/2b6baf9d466c4fd4a0a3075dd8d5c0cd
Publikováno v:
IEEE Access, Vol 11, Pp 82443-82448 (2023)
The brain performs cognitive functions through rhythmic communications of neural oscillations across numerous spatially distributed neurons. This process is known as “binding by synchrony”. Herein, we demonstrate oscillatory neural networks (ONNs
Externí odkaz:
https://doaj.org/article/2876892912304dd484eb797000506ed2
Publikováno v:
Nanomaterials, Vol 14, Iss 2, p 201 (2024)
A synaptic device with a multilayer structure is proposed to reduce the operating power of neuromorphic computing systems while maintaining a high-density integration. A simple metal–insulator–metal (MIM)-structured multilayer synaptic device is
Externí odkaz:
https://doaj.org/article/fd0aecb3b7ea4c33bac280727243b788
Publikováno v:
AIP Advances, Vol 13, Iss 1, Pp 015318-015318-6 (2023)
This paper investigated the conductance-state stability of TiN/PrCaMnOx (PCMO)-based resistive random-access memory (RRAM), which serves as a kernel weight element in convolutional neural networks (CNNs), to realize accurate feature extraction from i
Externí odkaz:
https://doaj.org/article/7cb3346d0edb423998fcc60551afc81e