Nature Electronics最新文献_第3页

A universal quantum electrical standard is getting closer 一个通用的量子电学标准越来越近了

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-26 DOI: 10.1038/s41928-025-01442-x

Wilfrid Poirier, Sophie Djordjevic

引用次数: 0

An issue of standards 标准问题

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-26 DOI: 10.1038/s41928-025-01453-8

引用次数: 0

Gigahertz topological phononic circuits based on micrometre-scale unsuspended waveguide arrays 基于微米尺度无悬浮波导阵列的千兆赫拓扑声子电路

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-25 DOI: 10.1038/s41928-025-01437-8

Xin-Biao Xu, Mourad Oudich, Yu Zeng, Ji-Zhe Zhang, Yuan-Hao Yang, Jia-Qi Wang, Weiting Wang, Luyan Sun, Guang-Can Guo, Yun Jing, Chang-Ling Zou

{"title":"Gigahertz topological phononic circuits based on micrometre-scale unsuspended waveguide arrays","authors":"Xin-Biao Xu, Mourad Oudich, Yu Zeng, Ji-Zhe Zhang, Yuan-Hao Yang, Jia-Qi Wang, Weiting Wang, Luyan Sun, Guang-Can Guo, Yun Jing, Chang-Ling Zou","doi":"10.1038/s41928-025-01437-8","DOIUrl":"10.1038/s41928-025-01437-8","url":null,"abstract":"The manipulation of gigahertz-frequency acoustic waves is of use in both classical and quantum applications. Topological phononics can provide robust acoustic control, but practical implementations are typically limited to low frequencies or lack scalability. Here we report reconfigurable topological phononic circuits that operate at 1.5 GHz. The approach is based on micrometre-scale unsuspended waveguides that tightly confine the acoustic waves. We use a custom-built high-resolution scanning optical vibrometer. Our visualization of the spatial evolution of topological edge states and robust Thouless pumping is in agreement with our theoretical analysis. We also develop a topological phononic Mach–Zehnder interferometer that can rapidly switch topological phonon transmission paths to provide acoustic intensity modulation with a 3 dB bandwidth of 0.65 kHz. Our work provides a reconfigurable, compact and scalable topological phononic chip that works at microwave frequencies. Reconfigurable topological phononic circuits that are based on micrometre-scale unsuspended waveguides and operate at 1.5 GHz can be used to create a topological phononic Mach–Zehnder interferometer that rapidly switches topological phonon transmission paths.","PeriodicalId":19064,"journal":{"name":"Nature Electronics","volume":"8 8","pages":"689-697"},"PeriodicalIF":40.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics 基于铌酸锂集成非线性声道的合成域计算和神经网络

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-25 DOI: 10.1038/s41928-025-01436-9

Jun Ji, Zichen Xi, Joseph G. Thomas, Bernadeta R. Srijanto, Ivan I. Kravchenko, Pranay Baikadi, Minglei Sun, William G. Vandenberghe, Ming Jin, Yizheng Zhu, Wenjie Xiong, Linbo Shao

{"title":"Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics","authors":"Jun Ji, Zichen Xi, Joseph G. Thomas, Bernadeta R. Srijanto, Ivan I. Kravchenko, Pranay Baikadi, Minglei Sun, William G. Vandenberghe, Ming Jin, Yizheng Zhu, Wenjie Xiong, Linbo Shao","doi":"10.1038/s41928-025-01436-9","DOIUrl":"10.1038/s41928-025-01436-9","url":null,"abstract":"Analogue computing uses the physical behaviours of devices to provide energy-efficient arithmetic operations. However, scaling up analogue computing platforms by simply increasing the number of devices leads to challenges such as device-to-device variation. Here we report scalable analogue computing and neural networks in the synthetic frequency domain using an integrated nonlinear phononic platform on lithium niobate. This synthetic-domain computing is robust to device variations, as vectors and matrices are concurrently encoded at different frequencies within a single device, achieving a high throughput per area. Leveraging inherent nonlinearities, our device-aware neural network can perform a four-class classification task with an accuracy of 98.2%. The nonlinear phononic computing hardware also maintains consistent performance over a wide operational temperature range (characterized up to 192 °C). Our synthetic-domain computing combines single-device parallelism, inherent nonlinearity and environmental stability, and could be of use in edge computing applications in which power efficiency and environmental resilience are crucial. By encoding vectors and matrices at different frequencies within a single device, a device-aware neural network based on lithium niobate integrated nonlinear phononics can be created that can perform a four-class classification task with an accuracy of 98.2%.","PeriodicalId":19064,"journal":{"name":"Nature Electronics","volume":"8 8","pages":"698-708"},"PeriodicalIF":40.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A soft magnetoelastic sensor to decode levels of fatigue 一个软磁弹性传感器来解码疲劳程度

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-21 DOI: 10.1038/s41928-025-01418-x

Jing Xu, Chrystal Duan, Xiao Wan, Ziyuan Che, Xun Zhao, Yihao Zhou, Yang Song, Junyi Yin, Trinny Tat, Song Li, Jun Chen

{"title":"A soft magnetoelastic sensor to decode levels of fatigue","authors":"Jing Xu, Chrystal Duan, Xiao Wan, Ziyuan Che, Xun Zhao, Yihao Zhou, Yang Song, Junyi Yin, Trinny Tat, Song Li, Jun Chen","doi":"10.1038/s41928-025-01418-x","DOIUrl":"10.1038/s41928-025-01418-x","url":null,"abstract":"Fatigue is a complex condition characterized by a decline in a person’s mental or physical performance. Methods to gauge fatigue include self-reported questionnaires, electroencephalography and camera-based technologies. However, these methods are typically restricted to laboratory settings, which limits their wider accessibility. Here we report a soft on-eyelid magnetoelastic sensor that can capture eye-blink parameters in real time and quantitatively decode fatigue levels. The sensor, which works in a self-powered manner, comprises a magnetomechanical coupling layer formed from a silicone rubber matrix embedded with micromagnets and a conductive gold coil patterned onto a thin thermoplastic elastomer layer. This design allows the conversion of eye movements into high-fidelity electrical signals. The sensor exhibits a Young’s modulus of 200 kPa, a stretchability of up to 530% and a pressure sensitivity of 0.2 µA kPa−1. Its thin membrane structure adheres conformally to human upper eyelid tissue and maintains intimate contact during diverse eye movements. When combined with a one-dimensional convolutional neural network and data-processing techniques, the sensor can recognize subtle eye movements and categorize fatigue levels with an accuracy of 96.4% based on six eye-blink parameters. An on-eyelid soft sensor that tracks eye movements based on the magnetoelastic effect can be used to continuously monitor and quantitatively assess levels of fatigue.","PeriodicalId":19064,"journal":{"name":"Nature Electronics","volume":"8 8","pages":"709-720"},"PeriodicalIF":40.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fatigue measurement via a wearable on-eyelid magnetoelastic sensor 基于可穿戴式眼睑磁弹性传感器的疲劳测量

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-20 DOI: 10.1038/s41928-025-01419-w

引用次数: 0

Interacting with computers through a wristband 通过腕带与电脑互动

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-19 DOI: 10.1038/s41928-025-01451-w

Yan Huang

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Indium selenide wafers for 2D electronics 用于二维电子器件的硒化铟晶圆

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-18 DOI: 10.1038/s41928-025-01450-x

Matthew Parker

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Bioinspired image recognition in neural networks 神经网络中的生物启发图像识别

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-14 DOI: 10.1038/s41928-025-01438-7

引用次数: 0

An integrated microwave neural network for broadband computation and communication 用于宽带计算与通信的集成微波神经网络

IF 40.9 1区工程技术

Nature Electronics Pub Date : 2025-08-14 DOI: 10.1038/s41928-025-01422-1

Bala Govind, Maxwell G. Anderson, Fan O. Wu, Peter L. McMahon, Alyssa Apsel

{"title":"An integrated microwave neural network for broadband computation and communication","authors":"Bala Govind, Maxwell G. Anderson, Fan O. Wu, Peter L. McMahon, Alyssa Apsel","doi":"10.1038/s41928-025-01422-1","DOIUrl":"10.1038/s41928-025-01422-1","url":null,"abstract":"The development of high-bandwidth applications, including multi-gigabit communication and radar imaging, demands faster processing. However, in the microwave regime, where frequencies exceed clock rates, sampling and computation become challenging. Here we report an integrated microwave neural network for broadband computation and communication. Our microwave neural network operates across tens of gigahertz but is reprogrammed with slow megabits per second control bitstreams. By exploiting strong nonlinearity in coupled microwave oscillations, it expresses its computation in a narrower spectrum, enabling easy read-out. The system searches bit sequences in multi-gigabits per second data and emulates digital functions without custom circuits. It accelerates radio-frequency machine learning by classifying encoding schemes and detecting frequency shifts to track flight trajectories from radar. The microwave neural network is fabricated with standard complementary metal–oxide–semiconductor technology. It occupies a sub-wavelength footprint of 0.088 mm2 on chip and has a sub-200-mW power consumption, supporting integration in a general-purpose analogue processor. A low-power microwave neural network fabricated using complementary metal–oxide–semiconductor technology can perform broadband computations using a slow control mechanism.","PeriodicalId":19064,"journal":{"name":"Nature Electronics","volume":"8 8","pages":"738-750"},"PeriodicalIF":40.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0