Nature Electronics

Journal: Nature Electronics

Abbreviation

Nat Electron

Publisher

Nature

ISSN

2520-1131

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Publications 1 - 10 of 17

A smart acoustic textile for health monitoring
Wang, Yingqiang; Sun, Chaochao; Ahmed, Daniel (2025)
Nature Electronics
Wearable electronics, such as smart textiles, are of potential use in healthcare monitoring, human-machine interfaces and environmental analysis. However, the scalability and reliability of the technology is restricted due to challenges related to rapid material degradation, potential toxicity, high production costs and heavy computational workload. Here we report an acoustic-based smart textile technology. The approach, which we term SonoTextiles, uses piezoelectric transducers that are mounted at both ends of glass microfibres and act as transmitters and receivers of acoustic waves. The flexible glass microfibres act as acoustic waveguides and are embedded into the textile substrate, providing precise sensing by measuring wave propagation and energy loss along the fibre in response to stimuli such as touch and bending. We also use acoustic frequency selectivity and frequency-domain signal processing algorithms to enhance computational efficiency. Our acoustic textile is breathable, durable and stable under thermal fluctuations, and we show that it can be used in distributed tactile sensing, hand gesture recognition and respiratory rate monitoring.
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
Moin, Ali; Zhou, Andy; Rahimi, Abbas; et al. (2021)
Nature Electronics
Wearable devices that monitor muscle activity based on surface electromyography could be of use in the development of hand gesture recognition applications. Such devices typically use machine-learning models, either locally or externally, for gesture classification. However, most devices with local processing cannot offer training and updating of the machine-learning model during use, resulting in suboptimal performance under practical conditions. Here we report a wearable surface electromyography biosensing system that is based on a screen-printed, conformal electrode array and has in-sensor adaptive learning capabilities. Our system implements a neuro-inspired hyperdimensional computing algorithm locally for real-time gesture classification, as well as model training and updating under variable conditions such as different arm positions and sensor replacement. The system can classify 13 hand gestures with 97.12% accuracy for two participants when training with a single trial per gesture. A high accuracy (92.87%) is preserved on expanding to 21 gestures, and accuracy is recovered by 9.5% by implementing model updates in response to varying conditions, without additional computation on an external device.
Opportunities and challenges for spintronics in the microelectronics industry
Dieny, Bernard; Gambardella, Pietro; et al. (2020)
Nature Electronics
Spintronic devices exploit the spin, as well as the charge, of electrons and could bring new capabilities to the microelectronics industry. However, in order for spintronic devices to meet the ever-increasing demands of the industry, innovation in terms of materials, processes and circuits are required. Here, we review recent developments in spintronics that could soon have an impact on the microelectronics and information technology industry. We highlight and explore four key areas: magnetic memories, magnetic sensors, radio-frequency and microwave devices, and logic and non-Boolean devices. We also discuss the challenges—at both the device and the system level—that need be addressed in order to integrate spintronic materials and functionalities into mainstream microelectronic platforms. © 2020 Springer Nature
A monolithic bipolar CMOS electronic-plasmonic high-speed transmitter
Koch, Ueli; Uhl, Christopher; Hettrich, Horst; et al. (2020)
Nature Electronics
In-memory hyperdimensional computing
Karuanaratne, Geethan; Le Gallo, Manuel; Cherubini, Giovanni; et al. (2020)
Nature Electronics
Photonically linked three-dimensional neural networks based on memristive blinking neurons
Zhou, Yue; Fang, Yuetong; Gisler, Raphael; et al. (2026)
Nature Electronics
The continuing development of artificial intelligence requires more powerful computing architectures. However, the large footprint of complementary-metal-oxide-semiconductor-based neurons and constraints on electric routing hinder the scaling of conventional artificial neurons and their synaptic connectivity. Here we show that memristive blinking neurons can be used to build scalable photonically linked three-dimensional neural networks. Our artificial neuron is based on a silver/poly(methyl methacrylate)/silver metal-insulator-metal memristive switching in-plane junction. Its resistive switching relies on atomic-scale filamentary dynamics and the device emits photon pulses on integrating a critical number of incoming electrical spikes, which eliminates the need for bulky peripheral circuit read-out and electrical wiring for transmitting signals. We use the memristive blinking neuron, which has a footprint of 170 nm x 240 nm, to build a photonically linked three-dimensional spiking neural network. We show that the network can perform a four-class classification task within the Google Speech dataset with an accuracy of 91.51%. We also create a high-density artificial neuron array with a pitch of 1 mu m and show that it can perform an MNIST classification task with an accuracy of 92.27%.
Stretchable and suturable fibre sensors for wireless monitoring of connective tissue strain
Lee, Jaehong; Ihle, Stephan J.; Pellegrino, Guglielmo S.; et al. (2021)
Nature Electronics
Implantable sensors can be used to monitor biomechanical strain continuously. However, three key challenges need to be addressed before they can be of use in clinical practice: the structural mismatch between the sensors and tissue or organs should be eliminated; a practical suturing attachment process should be developed; and the sensors should be equipped with wireless readout. Here, we report a wireless and suturable fibre strain-sensing system created by combining a capacitive fibre strain sensor with an inductive coil for wireless readout. The sensor is composed of two stretchable conductive fibres organized in a double helical structure with an empty core, and has a sensitivity of around 12. Mathematical analysis and simulation of the sensor can effectively predict its capacitive response and can be used to modulate performance according to the intended application. To illustrate the capabilities of the system, we use it to perform strain measurements on the Achilles tendon and knee ligament in an ex vivo and in vivo porcine leg.
What's next for FETs?
Luisier, Mathieu (2024)
Nature Electronics
Comprehensive device simulations reveal the potential of ultra-scaled field-effect transistors based on two-dimensional channel materials.
Colloidal quantum dot electronics
Liu, Mengxia; Yazdani, Nuri; Yarema, Maksym; et al. (2021)
Nature Electronics
The development of electronics is increasingly dependent on low-cost, flexible, solution-processed semiconductors. Colloidal quantum dots are solution-processed semiconducting nanocrystals that have a size-tunable bandgap and can be fabricated on a range of substrates. Here we review developments in colloidal quantum dot electronics, focusing on luminescent, optoelectronic, memory and thermoelectric devices. We examine the role of surface chemistry in the suppression of non-radiative processes, the control of light-matter interactions and the regulation of carrier transport properties. We also highlight the prospects of perovskite quantum dots as single-photon sources, the design of new classes of colloidal quantum dots and superlattices for emerging applications and the role of hybrid device architectures in compensating for the limited carrier mobility in colloidal quantum dot solids while maintaining their tunable spectral response.
Three-dimensional imaging of integrated circuits with macro- to nanoscale zoom
Holler, Mirko; Odstrčil, Michal; Guizar-Sicairos, Manuel; et al. (2019)
Nature Electronics

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