Results 171 to 180 of about 80,998 (291)

Improved I_on/I_off Current Ratio and Dynamic Resistance of a p-GaN High-Electron-Mobility Transistor Using an Al_0.5GaN Etch-Stop Layer. [PDF]

Materials (Basel), 2022
Wang HC, Liu CH, Huang CR, Shih MH, Chiu HC, Kao HL, Liu X.   +6 more
europepmc   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

On-Wafer Gate Screening Test for Improved Pre-Reliability in p-GaN HEMTs. [PDF]

Micromachines (Basel)
Giorgino G, Miccoli C, Cioni M, Reina S, Wakrim T, Guillon V, Sama NY, Gaillard P, Zeghouane M, Chauveau HJ, Castagna ME, Constant A, Iucolano F, Chini A.   +13 more
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

High Hole Concentration and Diffusion Suppression of Heavily Mg-Doped p-GaN for Application in Enhanced-Mode GaN HEMT. [PDF]

Nanomaterials (Basel), 2021
Dai JJ, Mai TT, Wu SK, Peng JR, Liu CW, Wen HC, Chou WC, Ho HC, Wang WF.   +8 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Optimization of Low-Voltage p-GaN Gate HEMTs for High-Efficiency Secondary Power Conversion. [PDF]

Micromachines (Basel)
Zhai L, Li X, Ji J, Yu L, Chen L, Chen Y, Xia H, Han Z, Wang J, Jiang X, Yuan S, Zhang T, Hao Y, Zhang J.   +13 more
europepmc   +1 more source

Improvement in the Output Power of Near-Ultraviolet LEDs of p-GaN Nanorods through SiO₂ Nanosphere Mask Lithography with the Dip-Coating Method. [PDF]

Nanomaterials (Basel), 2021
Yue W, Li P, Zhou X, Wang Y, Wu J, Bai J.   +5 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Research on the Degradation and Failure Mechanisms of the Unclamped-Inductive-Switching Characteristics of p-GaN HEMT Devices. [PDF]

Micromachines (Basel)
Liu L, Zhen Y, Li S, Pang B, Zeng K.
europepmc   +1 more source