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2019 Device Research Conference, DRC 2019

作者： Chen, Ying-Chen Hu, Szu- Tung Lin, Chao-Cheng Lee, Jack C. University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States Material Science and Engineering Program University of Texas at Austin AustinTX78712 United States Taiwan Semiconductor Research Institute TSRI Hsinchu Taiwan

ISBN: (纸本)9781728121123

Resistive random access memory (RRAM) using various metal oxides (i.e., SiO[1], HfO, NiO[2], AlO, TaO5 etc.) have attracted a great attention since the current nonvolatile memory (NVM) has been approaching the scaling limits. However, the sneak path current (SPC) is a main problem in crossbar memory configuration. The SPC significantly affects the read/write operation, because each word line is connected in perpendicular directions to the word line where the interference current from neighbor cells cannot be avoided in the crossbar configurations. In order to suppress the sneak path current in the array application, the transistor, diode, or selector device is designed to be integrated next to the memory cell for reducing the SPC. However, the additional selector device in the socalled 1T-1R or 1S-1R architecture (Fig. 1(a)) increases the cell size, process complexity, and cost. Therefore, the self-rectifying behavior i.e. nonlinear resistive switching (RS) in a 1R-only selectorless RRAM cell has been proposed [3]-[6]. However, the mechanisms in nonlinear characteristic and reliability properties are not yet been investigated. This work studies the reliability of early failure and relaxation properties using gap identification method. © 2019 IEEE.

关键词： Hafnium oxides

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ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

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IEEE Transactions on Image Processing 2023年 32卷 2843-2856页

作者： Chiou, Chien-Yu Lee, Kuang-Ting Huang, Chun-Rong Chung, Pau-Choo National Cheng Kung University Department of Electrical Engineering Tainan 701 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung 402 Taiwan National Cheng Kung University Cross College Elite Program and Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan 701 Taiwan

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://***/nchucvml/ADMM-SRNet. © 1992-2012 IEEE.

关键词： contrastive learning deep learning dictionary learning One-class classification sparse representation

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Impact Analysis of NUMA-Aware Flow Placement Based on Network Flow Size in Multicore Systems

Impact Analysis of NUMA-Aware Flow Placement Based on Networ...

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International Conference on Information and Communication Technology convergence (ICTC)

作者： Junseo Jang Jaehyun Hwang Department of Semiconductor Convergence Engineering Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper analyzes the impact of Direct Cache Access (DCA) within a multicore Non-Uniform Memory Access (NUMA) framework on network packet processing efficiency in Linux systems. By strategically placing network flows based on their characteristics and NUMA locality, we examine the performance dynamics of integrating and distributing long and short flows across different NUMA nodes. Utilizing Advanced Receive Flow Steering (aRFS) to differentiate and steer flows, we provide insights into the benefits of leveraging DCA for long flows and using DMA for short flows. Our study reveals significant improvements in network throughput by reducing data copying overheads, highlighting optimized traffic management strategies for high-demand data centers.

关键词： Data centers Multicore processing Linux Memory management Throughput Information and communication technology convergence

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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Temperature-Dependent Hydrogen Modulations of Ultra-Scaled a-IGZO Thin Film Transistor Under Gate Bias Stress

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2024年 5卷 9-16页

作者： Aslam, Muhammad Chang, Shu-Wei Chuang, Min-Hui Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan701401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results. © 2020 IEEE.

关键词： Oxide semiconductors

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Detection of Various Dental Conditions on Dental Panoramic Radiography Using Faster R-CNN

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IEEE Access 2023年 11卷 127388-127401页

作者： Chen, Shih-Lun Chen, Tsung-Yi Mao, Yi-Cheng Lin, Szu-Yin Huang, Ya-Yun Chen, Chiung-An Lin, Yuan-Jin Chuang, Mian-Heng Abu, Patricia Angela R. Chung Yuan Christian University Department of Electronic Engineering Taoyuan320314 Taiwan Feng Chia University Department of Electronic Engineering Taichung407102 Taiwan Chang Gung Memorial Hospital Department of General Dentistry Taoyuan61363 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing Department of Program on Semiconductor Manufacturing Technology National Cheng Kung University Tainan717005 Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei243303 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon1108 Philippines

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model's accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions. © 2013 IEEE.

关键词： Image segmentation

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Epidermal piezoresistive structure with deep learning-assisted data translation

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npj Flexible Electronics 2022年第1期6卷 672-680页

作者： Changrok So Jong Uk Kim Haiwen Luan Sang Uk Park Hyochan Kim Seungyong Han Doyoung Kim Changhwan Shin Tae-il Kim Wi Hyoung Lee Yoonseok Park Keun Heo Hyoung Won Baac Jong Hwan Ko Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan University(SKKU)Suwon 16419Republic of Korea School of Chemical Engineering Sungkyunkwan University(SKKU)Suwon 16419Republic of Korea Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Ajou UniversitySuwon 16499Republic of Korea School of Electrical Engineering Korea UniversitySeoul 02841Republic of Korea Biomedical Institute for Convergence(BICS) Sungkyunkwan University(SKKU)Suwon 16419Korea Department of Organic and Nano System Engineering Konkuk UniversitySeoul 05029Republic of Korea Department of Advanced Materials Engineering for Information and Electronics Kyung Hee UniversityYongin 17104Republic of Korea School of Semiconductor and Chemical Engineering Jeonbuk National UniversityJeonju 54896Republic of Korea

Continued research on the epidermal electronic sensor aims to develop sophisticated platforms that reproduce key multimodal responses in human skin,with the ability to sense various external stimuli,such as pressure,shear,torsion,and *** development of such applications utilizes algorithmic interpretations to analyze the complex stimulus shape,magnitude,and various moduli of the epidermis,requiring multiple complex equations for the attached *** this experiment,we integrate silicon piezoresistors with a customized deep learning data process to facilitate in the precise evaluation and assessment of various stimuli without the need for such *** the ability to surpass conventional vanilla deep regression models,the customized regression and classification model is capable of predicting the magnitude of the external force,epidermal hardness and object shape with an average mean absolute percentage error and accuracy of<15 and 96.9%,*** technical ability of the deep learning-aided sensor and the consequent accurate data process provide important foundations for the future sensory electronic system.

关键词： utilize attached precise

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Improving Light Field Reconstruction from Limited Focal Stack Using Diffusion Models

Improving Light Field Reconstruction from Limited Focal Stac...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Hyung Sup Yun Il Yong Chun Department of Electrical and Computer Engineering Sungkyunkwan University Department of Artificial Intelligence Semiconductor Convergence Engineering and Display Convergence Engineering School of Electronic and Electrical Engineering Sungkyunkwan University Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon Republic of Korea

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Light field (LF) imaging has various a pplications in image processing and computer vision. There has been growing interest in reconstructing LF from focal stack (FS) images, as directly capturing LFs with a camera array or micro-lens array has a trade-off between spatial and angular resolution in LFs. However, accurate LF reconstruction using limited noisy FS is challenging since it is a highly ill-conditioned/posed inverse problem. This paper proposes the first diffusion model based LF reconstruction approach using FS, specifically, Denoising Diffusion LF Reconstruction Model (DDLFRM). We propose 1) a new tractable system matrix decomposition method for LF reconstruction from FS to use Denoising Diffusion Restoration Model updates, and 2) a LF characteristics based U-Net architecture for LF generation. Numerical results show that the proposed DDLFRM framework achieves significantly improved LF reconstruction accuracies compared with state-of-the-art/existing LF reconstruction methods.

关键词： Accuracy Inverse problems Computational modeling Noise reduction Reconstruction algorithms Diffusion models Light fields Numerical models Noise measurement Image reconstruction

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Multi-bit functional NOR type SONOS memories

Multi-bit functional NOR type SONOS memories

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66th DRC Device Research Conference Digest, DRC 2008

作者： Kim, Moonkyung Kim, Chung Woo Lee, Jo-Won Tiwari, S. Department of Electrical Computer Engineering Cornell University Ithaca NY 14853 Semiconductor R and D Center Memory Business Samsung Electronics Co. Ltd. Korea Republic of National Program for Tera-level Nano Devices Korea Republic of

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Towards Eight-Bit Quantization for 3D U-Net Medical Image Segmentation via ROI-Based Calibration and Background-Aware Shift

Towards Eight-Bit Quantization for 3D U-Net Medical Image Se...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Soosung Kim Mincheol Cha Chaehag Yi Xuan Truong Nguyen Department of Electrical and Computer Engineering Chungang University Seoul Korea Department of Electrical and Computer Engineering Seoul National University Seoul Korea Department of Next Generation Semiconductor Convergence and Open Sharing System Seoul National University Seoul Korea

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

3D U-Net medical image segmentation is essential for precise diagnosis and treatment planning but comes with massive bandwidth requirements and considerable computational costs. Eight-bit fixed-point quantization is crucial for efficient 3D U-Net inference on modern deep learning accelerators. However, quantizing a 3D U-Net model is non-trivial and frequently leads to unfavorable accuracy degradation. We empirically observed that large range imbalances between tumor/non-tumor voxels and foreground/background areas are the main contributing factors. Based on these findings, we propose an eight-bit quantization method with a simple yet effective ROI-based calibration and a background shift that captures outliers in tumor areas to avoid accuracy degradation. Moreover, our method incorporates instance normalization folding, which eliminates computation-intensive operations during inference. Experimental results on the KITS19 dataset show that the proposed method with integer-only arithmetic achieves a negligible accuracy drop, i.e., 0.4%p in the dice score, compared to the FP32 model.

关键词： Degradation Image segmentation Solid modeling Three-dimensional displays Quantization (signal) Accuracy Computational modeling Calibration Medical diagnostic imaging Tumors

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