Zobrazeno 1 - 10
of 1 718
pro vyhledávání: '"charge trap"'
Autor:
Mohammad Sedghi, Camilla Vael, Wei-Hsu Hu, Michael Bauer, Daniele Padula, Alessandro Landi, Mirko Lukovic, Matthias Diethelm, Gert-Jan Wetzelaer, Paul W. M. Blom, Frank Nüesch, Roland Hany
Publikováno v:
Science and Technology of Advanced Materials, Vol 25, Iss 1 (2024)
ABSTRACTAlready in 2012, Blom et al. reported (Nature Materials 2012, 11, 882) in semiconducting polymers on a general electron-trap density of ≈3 × 1017 cm−3, centered at an energy of ≈3.6 eV below vacuum. It was suggested that traps have an
Externí odkaz:
https://doaj.org/article/e0b26dbfc2d04c0fa688cbe616252d5c
Publikováno v:
IEEE Access, Vol 12, Pp 15050-15055 (2024)
As NAND flash evolved from two-dimensional (2D) to three-dimensional (3D), all cells have been changed to share a charge trap layer (CTL). This change has a lateral charge spreading effect, which is the trapped charge spreading laterally. This latera
Externí odkaz:
https://doaj.org/article/6f613eab578547038357eb8d0d83695c
Autor:
Kean Chuan Lee, Martin Weis
Publikováno v:
Inorganics, Vol 12, Iss 10, p 257 (2024)
Wide-bandgap semiconductors have been envisioned for power electronics applications because of their ability to operate at higher temperatures and higher applied voltages without breakdown. However, the presence of defects may cause device failure, n
Externí odkaz:
https://doaj.org/article/f0441a942797490190c26e1ea9ddbc6f
Publikováno v:
Materials Today Advances, Vol 20, Iss , Pp 100421- (2023)
In the human brain, attention plays a crucial role in encoding information into memory. Therefore, focused attention during encoding enhances the likelihood of information being effectively encoded and stored in memory. This phenomenon is creatively
Externí odkaz:
https://doaj.org/article/f7869e42ef19423b9a7d94bcfd1dcdab
Autor:
Song Lee, Jeong-In Lee, Chang-Hyun Kim, Jin-Hyuk Kwon, Jonghee Lee, Amos Amoako Boampong, Min-Hoi Kim
Publikováno v:
Science and Technology of Advanced Materials, Vol 24, Iss 1 (2023)
ABSTRACTThe charge trap property of solution-processed zirconium acetylacetonate (ZAA) for solution-processed nonvolatile charge-trap memory (CTM) transistors is demonstrated. Increasing the annealing temperature of the ZAA from room temperature (RT)
Externí odkaz:
https://doaj.org/article/2ceebd091e2f4a06b67429d772ebb84a
Publikováno v:
Biomimetics, Vol 9, Iss 6, p 335 (2024)
To mimic the homeostatic functionality of biological neurons, a split-gate field-effect transistor (S-G FET) with a charge trap layer is proposed within a neuron circuit. By adjusting the number of charges trapped in the Si3N4 layer, the threshold vo
Externí odkaz:
https://doaj.org/article/1c9c834bf3ca4bd3ac53ab473a1592d0
Autor:
Zeyang Xiang, Kexiang Wang, Jie Lu, Zixuan Wang, Huilin Jin, Ranping Li, Mengrui Shi, Liuxuan Wu, Fuyu Yan, Ran Jiang
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2588 (2024)
In this work, the implementation of HfZrO layers for the tunneling, charge trapping, and blocking mechanisms within the device offer benefits in terms of programmability and data retention. This configuration has resulted in a memory device that can
Externí odkaz:
https://doaj.org/article/58524cfb35514c449f7ebcd073678929
Autor:
Yongmin Baek, Byungjoon Bae, Jeongyong Yang, Doeon Lee, Hee Sung Lee, Minseong Park, Taegeon Kim, Sihwan Kim, Bo‐In Park, Geonwook Yoo, Kyusang Lee
Publikováno v:
Advanced Electronic Materials, Vol 9, Iss 11, Pp n/a-n/a (2023)
Abstract Artificial neural networks (ANNs) are widely used in numerous artificial intelligence‐based applications. However, the significant amount of data transferred between computing units and storage has limited the widespread deployment of ANN
Externí odkaz:
https://doaj.org/article/1304a604e30042ffb8932070e7b8eee1
Publikováno v:
Advanced Science, Vol 10, Iss 32, Pp n/a-n/a (2023)
Abstract The progress of artificial intelligence and the development of large‐scale neural networks have significantly increased computational costs and energy consumption. To address these challenges, researchers are exploring low‐power neural n
Externí odkaz:
https://doaj.org/article/0f24a25e1b5d47a4acee1135f1317b07
Autor:
Kihoon Nam, Chanyang Park, Hyeok Yun, Jun-Sik Yoon, Hyundong Jang, Kyeongrae Cho, Min Sang Park, Hyun-Chul Choi, Rock-Hyun Baek
Publikováno v:
IEEE Access, Vol 11, Pp 7135-7144 (2023)
A machine learning (ML) method was used to optimize the trap distribution of the charge trap nitride (CTN) to simultaneously improve its performance/reliability (P/R) characteristics, which are tradeoffs in 3-D NAND flash memories. Using an artificia
Externí odkaz:
https://doaj.org/article/3eb69a874bce4bdcbb11a9e92e3efa02