Results 51 to 60 of about 5,000 (223)

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

Structural Influence on Radiation-induced Single-event Effects in SiC MOSFETs: Comparative Analysis of Planar and Trench Designs

Yuanzineng kexue jishu
The single-event susceptibility of three silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) power devices structures (planar, trench and double trench) is researched by the technology computer-aided design (TCAD) simulation.
HU Libin1, 2, FENG Shaohui3, SUI Chenglong3, WANG Chengjie3, CHEN Miao3, LU Peng1, YANG Can1, SHU Lei1, , LU Jiang4, LI Bo1, 2
doaj   +1 more source

Physics‐Based Compact Modeling of Advanced 3D Nanoscale Vertical NAND Flash Memory

Advanced Electronic Materials, EarlyView.
For advanced 3D NAND flash memory, a unified compact model for SPICE is proposed that spans from the intrinsic unit cell to the full string and captures the electrostatic coupling with adjacent inhibit strings. It can successfully predict read behavior, program/erase dynamics, and interactions between neighboring cells, reflecting array‐level behavior ...
Ilho Myeong, Seonho Shin, Ickhyun Song
wiley   +1 more source

Computational Study of the Electronic Performance of Cross-Plane Superlattice Peltier Devices [PDF]

, 2011
We use a state-of-the-art non-equilibrium quantum transport simulation code, NEMO- 1D, to address the device physics and performance benchmarking of cross-plane superlattice Peltier coolers.
Gerhard Klimeck, Lundstrom, M., Jeong, Changwook, Klimeck, Gerhard, Klimeck, G., Changwook Jeong, Mark Lundstrom, Lundstrom, Mark S.   +7 more
core   +2 more sources

Exploiting Temperature Effects for Robust Control and Reference Circuits Using Thin‐Film Contact‐Controlled Transistors

Advanced Electronic Materials, EarlyView.
Compact circuits based on contact‐controlled transistors are well‐suited to unsupervised thermal management, sensitive temperature measurement, or temperature‐stable current references. Demonstrated on flexible microcrystalline silicon and supported by simulation, the approach does not require supply voltage regulation, remains manufacturable across ...
Eva Bestelink, Jean‐Charles Fustec, Olivier de Sagazan, Rogier Fransen, George Bairaktatis, Emmanuel Jacques, Radu A. Sporea   +6 more
wiley   +1 more source

A TCAD tool for the simulation of the CVD process based on cellular automata [PDF]

, 2001
The development of next-generation VLSI circuits with deep submicron technologies demands fundamental understanding of the wafer surface reaction kinetics.
I. Karafyllidis, A. Thanailakis, G.Ch. Sirakoulis   +2 more
core   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Tandem Neural Network Rapidly Solves Multivalued Inverse Problems: Application to Oxide‐Semiconductor Characterization

Advanced Intelligent Systems, EarlyView.
A tandem neural network directly solves the multivalued inverse problem of extracting semiconductor parameters from transistor measurements. Trained on only 1000 simulations, the network infers six material parameters (e.g., defect states, carrier concentration, mobility) in under 1 ms, demonstrating a broadly applicable framework for semiconductor ...
Masatoshi Kimura, Keisuke Ide, Kuan‐Ju Zhou, Atsushi Shimizu, Takayoshi Katase, Hidenori Hiramatsu, Kei Terayama, Hideo Hosono, Toshio Kamiya   +8 more
wiley   +1 more source

Computationally Driven Advances in Cu‐CNT On‐Chip Interconnect Materials: From First Principles to Machine Learning

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A multiscale framework integrating electronic, mechanical, and thermal analysis with machine learning to optimize carbon nanotube interconnects. As the component dimensions in integrated circuits shrink to extreme scales, the complexity of interconnect systems is increasing significantly, necessitating an urgent and comprehensive upgrade of ...
Changhong Zhang, Jie Liang, Yi Li, Zhenyu Wang, Haoyuan Li, Zhen Yuan, Jiuren Zhou, Rongmei Chen, Wensheng Zhao, Lei Zhang, Hanshen Xin, Jianhua Zhang   +11 more
wiley   +1 more source

Real-time TCAD: A new paradigm for TCAD in the artificial intelligence era [PDF]

, 2020
This paper presents a novel approach to enable real-time device simulation and optimization. State-of-the-art algorithms which can describe semiconductor domain are adopted to train deep learning models whose input and output are process condition and ...
Huh, In, Kim, Yoon-Suk, Kim, Jae-Ho, Myung, Sanghoon, Jeong, Changwook, Kim, Jinwoo, Kim, Jaemin, Ryu, Jisu, Kim, Dae Sin, Jang, Wonik, Jeon, Yongwoo, Doh, Jiseong, Han, Songyi, Baek, Kang-Hyun   +13 more
core   +1 more source