Junwei Luo
Chinese Academy of Sciences
21 Papers
Junwei Luo is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Chiral Mesostructured NiO Films with Spin Polarisation
TL;DR: In this paper, spin polarisation is found in the centrosymmetric nonferromagnetic crystals, chiral mesostructured NiO films (CMNFs), fabricated through the symmetry-breaking effect of a chiral molecule.
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On the Understanding of pMOS NBTI Degradation in Advance Nodes: Characterization, Modeling, and Exploration on the Physical Origin of Defects
Yongkang Xue,Pengpeng Ren,Zhuyou Liu,Shuying Wang,Yu Li,Zirui Wang,Zixuan Sun,Da Wang,Yichen Wen,Shiyu Xia,Lining Zhang,Jian Zhang,Zhi-xin Ji,Junwei Luo,Huixiong Deng,Runsheng Wang,Lianfeng Yang,Ru Huang +17 more
TL;DR: Researchers characterized and modeled four types of traps in 7nm pFinFETs under NBTI stress, identifying their physical origins and proposing a unified aging prediction framework for long-term predictive capability and DTCO in advanced nodes.
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Direct bandgap emission from strain-doped germanium
Linding Yuan,Shu-Shen Li,Junwei Luo +2 more
TL;DR: Researchers propose incorporating lithium atoms into germanium to induce tensile strain, converting its indirect bandgap to a direct bandgap for efficient light emission, with potential applications in Si-compatible optoelectronics and photonics.
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Cryogenic Mobility Enhancement in Si MOS Devices via SiO2 Regrowth
Shuai Zhao,Guodong Yuan,Qiuhao Zhu,L. Song,Di Zhang,Yumeng Liu,Jun Lou,Weihua Han,Junwei Luo +8 more
TL;DR: In this paper , the adverse impacts of reactive ion etching (RIE) on MOS device cryogenic mobilities were investigated. And the authors provided a feasible integration flow to recover device performances, which may promote the evolution of Si-based MOS quantum dot computation.
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Toward Reliability-and Variability-Aware Design-Technology Co-Optimization in Advanced Nodes: Defect Characterization, Industry-Friendly Modeling, and ML-Assisted Prediction
Zhi-xin Ji,Yongkang Xue,Pengpeng Ren,Jinfeng Ye,Yu Li,Yi Shan Wu,Da Wang,Shuying Wang,Junjie Wu,Zirui Wang,Yichen Wen,Shiyu Xia,Lining Zhang,Jian Zhang,Junhua Liu,Junwei Luo,Huixiong Deng,Runsheng Wang,Lianfeng Yang,Ru Huang +19 more
TL;DR: This work tackles issues by developing an efficient characterization method for separating defects, introducing a comprehensive test-data-verified defect-centric physical-based model and an industry-friendly open model interface (OMI)-based compact model, and proposing a machine learning (ML)-assisted approach to accelerate circuit-level prediction.
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