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651 results on '"WANG, ZHIXUAN"'

1. A Novel Energy-Efficient Salicide-Enhanced Tunnel Device Technology Based on 300mm Foundry Platform Towards AIoT Applications

Author

Wang, Kaifeng, Huang, Qianqian, Wu, Yongqin, Ren, Ye, Wei, Renjie, Wang, Zhixuan, Yang, Libo, Zhang, Fangxing, Geng, Kexing, Li, Yiqing, Yang, Mengxuan, Luo, Jin, Liu, Ying, Zheng, Kai, Kang, Jin, Ye, Le, Zhang, Lining, Bu, Weihai, and Huang, Ru

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

This work demonstrates a novel energy-efficient tunnel FET (TFET)-CMOS hybrid foundry platform for ultralow-power AIoT applications. By utilizing the proposed monolithic integration process, the novel complementary n and p-type Si TFET technology with dopant segregated source junction and self-aligned drain underlap design is successfully integrated into a 300mm CMOS baseline process without CMOS performance penalty and any new materials, experimentally demonstrating the large Ion and record high Ion/Ioff ratio of 10^7 among TFETs by industry-manufacturers. The device performance and variability are also co-optimized for high-volume production. Further circuit-level implementations are presented based on the calibrated compact model. The proposed TFET-CMOS hybrid logic and SRAM topologies show significant energy efficiency improvement with comparable operation speed compared with standard CMOS circuits, indicating its great potential for power-constraint AIoT applications.

Published
2024

3. Advancing Multimodal Data Fusion in Pain Recognition: A Strategy Leveraging Statistical Correlation and Human-Centered Perspectives

Author

Gu, Xingrui, Wang, Zhixuan, Jin, Irisa, and Wu, Zekun

Subjects
Computer Science - Artificial Intelligence
Abstract

This research presents a novel multimodal data fusion methodology for pain behavior recognition, integrating statistical correlation analysis with human-centered insights. Our approach introduces two key innovations: 1) integrating data-driven statistical relevance weights into the fusion strategy to effectively utilize complementary information from heterogeneous modalities, and 2) incorporating human-centric movement characteristics into multimodal representation learning for detailed modeling of pain behaviors. Validated across various deep learning architectures, our method demonstrates superior performance and broad applicability. We propose a customizable framework that aligns each modality with a suitable classifier based on statistical significance, advancing personalized and effective multimodal fusion. Furthermore, our methodology provides explainable analysis of multimodal data, contributing to interpretable and explainable AI in healthcare. By highlighting the importance of data diversity and modality-specific representations, we enhance traditional fusion techniques and set new standards for recognizing complex pain behaviors. Our findings have significant implications for promoting patient-centered healthcare interventions and supporting explainable clinical decision-making., Comment: Accepted by AHRI 2024

Published
2024

28. Transport-Induced-Charge Electroosmosis

Author

Hsu, Wei-Lun, Wang, Zhixuan, and Daiguji, Hirofumi

Subjects
Physics - Fluid Dynamics
Abstract

We report theoretical analysis of transport-induced-charge electroosmosis (TICEO) in a nanopore due to the presence of a local electric field and a conductivity gradient. TICEO shares a similar characteristic with classical induced-charge electroosmosis (ICEO) that the mean velocity $\overline{v_\text{TIC}}$ is proportional to the square of the applied electric potential difference $\Delta\psi$, $i.e.$, $\overline{v_\text{TIC}} \propto (\Delta\psi)^2$, appropriate for alternating current (AC) pumping applications. In contrast to ICEO which is primarily used in microfluidics, TICEO does not require metallic or dielectric patches and is thus suitable for nanopore pumping, providing new opportunities for AC nanopore applications., Comment: 5 pages; 4 figures

Published
2021

47. Investigation of Nuclear Binding Energy and Charge Radius Based on Random Forest Algorithm

Author

CAI Boshuai;YU Tianjun;LIN Xuan;ZHANG Jilong;WANG Zhixuan;YUAN Cenxi

Subjects
nuclear binding energy, nuclear charge radius, Nuclear engineering. Atomic power, TK9001-9401, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The random forest algorithm was applied to study the nuclear binding energy and charge radius. The regularized root-mean-square of error (RMSE) was proposed to avoid overfitting during the training of random forest. RMSE for nuclides with Z, N>7 is reduced to 0.816 MeV and 0.020 0 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula, respectively. Specific interest is in the possible (sub) shells among the superheavy region, which is important for searching for new elements and the island of stability. The significance of shell features estimated by the so-called shapely additive explanation method suggests (Z, N)=(92, 142) and (98, 156) as possible subshells indicated by the binding energy. Because the present observed data is far from the N=184 shell, which is suggested by mean-field investigations, its shell effect is not predicted based on present training. The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells. The effect is verified by the shell-corrected nuclear charge radius model.

Published
2023
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