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Nature 2025年第8059期640卷 613-622页

作者： Lanza, Mario Pazos, Sebastian Aguirre, Fernando Sebastian, Abu Le Gallo, Manuel Alam, Syed M. Ikegawa, Sumio Yang, J. Joshua Vianello, Elisa Chang, Meng-Fan Molas, Gabriel Naveh, Ishai Ielmini, Daniele Liu, Ming Roldan, Juan B. Department of Materials Science and Engineering National University of Singapore Singapore Singapore Institute for Functional Intelligent Materials National University of Singapore Singapore Singapore Centre for Advanced 2D Materials (CA2DM) National University of Singapore Singapore Singapore Materials Science and Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Intrinsic Semiconductor Technologies London United Kingdom IBM Research – Zurich Rüschlikon Switzerland Everspin Technologies Chandler AZ United States Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States CEA-Leti Université Grenoble Alpes Grenoble France Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Weebit Nano Hod Hasharon Israel Dipartimento di Elettronica Informazione e Bioingegneria (DEIB) Politecnico di Milano and *** Milano Italy Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing China Departamento de Electrónica y Tecnología de Computadores Universidad de Granada Facultad de Ciencias Granada Spain

The semiconductor industry is experiencing an accelerated transformation to overcome the scaling limits of the transistor and to adapt to new requirements in terms of data storage and computation, especially driven by artificial intelligence applications and the Internet of Things. In this process, new materials, devices, integration strategies and system architectures are being developed and optimized. Among them, memristive devices and circuits—memristors are two-terminal memory devices that can also mimic some basic bioelectronic functions—offer a potential approach to create more compact, energy-efficient or better-performing systems. The memristor industry is growing quickly, raising abundant capital investment, creating new jobs and placing advanced products in the market. Here we analyse the status and prospects of the memristor industry, focusing on memristor-based products that are already commercially available, prototypes with a high technological readiness level that might affect the market in the near future, and discuss obstacles and pathways to their implementation. © Springer Nature Limited 2025.

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A Low Power Digitizer Array with Adaptive Split Current Source for 3-D-Stacked 100 MP High Dynamic Range Imager

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IEEE Sensors Journal 2025年

作者： Lee, Youngwoo Kim, Seyeon Kim, Jinha Kim, Suhwan Jun, Jaehoon Seoul08826 Korea Republic of Samsung Electronics Hwaseong18448 Korea Republic of Inha University Department of Electrical and Computer Engineering Program in Semiconductor Convergence Incheon22212 Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Gwanak Analog Company Ltd Seoul08826 Korea Republic of

The increasing demand for high dynamic range (HDR), power efficiency, and high resolution has driven the adoption of single-exposure dual conversion gain (DCG) techniques. This paper proposes an adaptive split current source to optimize power consumption in single-exposure DCG readouts. By splitting the comparator bias current, the power consumption of the digitizer array can be adaptively optimized based on the conversion gain (CG) of pixel in single-exposure DCG operation. Additional power-saving features including decision-feedback and auto-zeroing (AZ) power-down techniques are also implemented to further improve power efficiency. The proposed digitizer chip was fabricated in a 28 nm CMOS process, achieving a power consumption reduction of 44.5% in comparator. The integral nonlinearity (INL) was measured as +2.67/–2.34 LSB in high conversion gain (HCG) and +2.95/–1.92 LSB in low conversion gain (LCG). The input-referred random noise (RN) values of 2.17 LSB (HCG) and 2.41 LSB (LCG) were measured at an analog gain of 16, corresponding to 93 μVrms and 103 μVrms, respectively. The prototype chip shows a highly competitive figure of merit (FoM) of 2.41 mV∙pJ/pixel/frame. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Corrigendum to “Does a large response suffice?: Thermally stable and low noise Si-doped IZO thin-film transistor-type gas sensors” [Sens. Actuators: B Chem. 422 (2025) 136498]

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Sensors and Actuators B: Chemical 2025年 426卷

作者： Wonjun Shin Sunjin Lee Ryun-Han Koo Jangsaeng Kim Sang Yeol Lee Sung-Tae Lee Department of Semiconductor Convergence Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center Seoul National University Seoul 08826 Republic of Korea Department of Semiconductor Engineering Gachon University Seongnam-Si Gyeonggi-Do 13120 Republic of Korea Gachon Advanced Institute of Semiconductor Technology Gachon University Seongnam-Si Gyeonggi-Do 13120 Republic of Korea Electronic Engineering Department Gachon University Seongnam 13120 Republic of Korea Department of Electronic and Electrical Engineering Hongik University Seoul 04066 Republic of Korea

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Impact Analysis of NUMA-Aware Flow Placement Based on Network Flow Size in Multicore Systems 15

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

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15th International Conference on Information and Communication Technology convergence, ICTC 2024

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

ISBN: (纸本)9798350364637

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. © 2024 IEEE.

关键词： Linux

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Form Factor–Free Magneto-Triboelectric Generator for Standalone Power Line IoT Applications

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Advanced Energy Materials 2025年

作者： Kim, Hyun Soo Lee, Min Hyuk Kim, Do-Heon Lee, Dong-Gyu Imani, Iman M. Hur, SungHoon Ko, Young Joon Choi, Yeong Uk Cho, Hyunah Song, So-Min Yoon, Tae Kyoung Oh, In Woo Jung, Jong Hoon Chen, Jun Kim, Yunseok Kang, Heemin Ryu, Jungho Baik, Jeong Min Song, Hyun-Cheol Seoul02792 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of Suwon16419 Korea Republic of Department of Materials Science and Engineering Korea University Seoul02841 Korea Republic of Division of Energy & Environment Technology University of Science and Technology Seoul02792 Korea Republic of Suwon16419 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Department of Mechanical Engineering Korea University Seoul02841 Korea Republic of Department of Bioengineering University of California Los Angeles Los AngelesCA90095 United States Research Center for Advanced Materials Technology Core Research Institute Suwon16419 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongsan38541 Korea Republic of Program in Semiconductor Convergence Inha University Incheon22212 Korea Republic of Department of Future Convergence Materials Korea University Seoul02841 Korea Republic of College of Medicine Korea University Seoul02841 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of

The growing reliance on electronic devices has made ambient magnetic field harvesting a promising solution for powering low-power, small-scale technologies, such as those used in the Internet of Things (IoT). While metal alloy-based magneto-deformation materials have traditionally been used to capture energy from stray magnetic fields, they are costly and lack versatility. To advance magnetic field harvesting, it is essential to develop cost-effective, high-performance, and adaptable magneto-deformation materials. Incorporating ferromagnetic metal powders into polymers can induce magneto-rheological behavior. This quasi-solid magneto-rheological effect enables the generation of mechanical vibrations in response to an oscillating external magnetic field. Here, a functional composite film is presented that achieves efficient and straightforward magneto-deformation by integrating Fe powder with poly(vinylidene fluoride-trifluoroethylene). To further enhance the performance of the composite film, MoS2–SiO2 core–shell nanoparticles is exploited for improved charge trapping and employ ferroelectrics to increase the contact potential difference (CPD). The composite film shows a bending displacement of 1 mm in a 4 Oe magnetic field, with each magneto-triboelectric module generating 14.28 mW. The four fabricated modules successfully harvest real-time energy from the stray magnetic field of an electric pot, enabling a battery-free Bluetooth IoT sensor. © 2025 The Author(s). Advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Magnetoresistance

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A Low-Noise Capacitively-Coupled Chopper Instrumentation Amplifier with SAR-Assisted Low Current Ripple Reduction Technique 21

A Low-Noise Capacitively-Coupled Chopper Instrumentation Amp...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Jo, Jungkook Jun, Jaehoon Inha University Department of Electrical and Computer Engineering Korea Republic of Inha University Program in Semiconductor and Device Korea Republic of

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377088

This paper presents a novel design technique for reducing ripple in various sensor read-out integrated circuits (IC) by calibrating the amplifier offset through current control in bias resistors. Traditional methods for reducing ripple often involve complex amplifiers consuming static power. In contrast, the proposed ripple reduction technique employs successive-approximation register (SAR) logic to control the current flowing through the bias resistor, thus minimizing static power consumption. Experimental results obtained through simulations in a 28 nm CMOS process demonstrate significant ripple reduction to 8.94 μVpp from 2 mVpp. This approach is suitable for low-power design, particularly in wearable device applications, and can potentially extend to other amplifier configurations utilizing chopping technique. © 2024 IEEE.

关键词： Chopper amplifiers

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

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

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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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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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

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

ISBN: (纸本)9798331530839

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. © 2024 IEEE.

关键词： Image segmentation

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Photon avalanche-driven multicolor emission: yttrium vanadate upconversion phosphors for optical thermometry, forensics, and healing light applications

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Chemical engineering Journal 2025年

作者： Eluri Pavitra Lintymol Antony Jin Young Park Seung Kyu Hwang Jeong-Hwan Lee Gogineni Rajesh Chandra M.V. Basaveswara Rao Ganji Seeta Rama Raju Young-Kyu Han Yun Suk Huh NanoBio High-Tech Materials Research Center Department of Biological Sciences and Bioengineering Inha University Incheon 22212 Republic of Korea 3D Convergence Center Inha University Incheon 22212 Republic of Korea Department of Energy and Materials Engineering Dongguk University-Seoul Seoul 04620 Republic of Korea Department of Electrical Electronics and Software Engineering Pukyong National University Yongdang Campus Busan 48547 Republic of Korea Department of Materials Science and Engineering Inha University Incheon 22212 Republic of Korea Program in Semiconductor Convergence Inha University Incheon 22212 Republic of Korea Department of Computer Science and Engineering Vignan’s Lara Institute of Technology & Science Guntur Andhra Pradesh India Department of Chemistry Krishna University Machillipatnam Andhra Pradesh 521001 India

The exploration of photon upconversion has led to the development of multifunctional lanthanide-based materials, particularly through the mechanism of photon avalanche, marking a transformative advancement in material science. In this study, yttrium vanadate upconversion nanocrystalline phosphors (UCNCPs) activated by Ho 3+ /Er 3+ /Tm 3+ /Yb 3+ ions are reported. These UCNCPs demonstrate a remarkable ability in the photon avalanche, offering highly intense and tunable emissions across the visible spectrum at room temperature under low excitation power of 980 nm laser. The average number of photons involved in the UC process at threshold pump power, highlighting the intricate interplay of cross-relaxation and energy transfer loops in avalanche mechanism. Notably, these UCNCPs have shown exceptional temperature sensing capabilities, where non-thermally coupled levels of the Ho 3+ and Tm 3+ ions provide higher relative sensitivity and accuracy than thermally coupled levels of Er 3+ ions. The versatility of these UCNCPs is further established by their application in solid-state lighting, where they can produce vibrant red, green, blue, and warm-white light, and in security, serving as multi-level anti-counterfeiting inks that respond to 980 nm laser illumination. These developments not only pave the way for more energy-efficient lighting solutions but also offer new avenues for enhancing the security of valuable items. The temperature sensing capabilities of these phosphors add another layer of functionality, making them ideal for high-tech applications ranging from medical diagnostics to environmental monitoring.

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