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Infrared in-sensor computing Based on Flexible Photothermoelectric Tellurium Nanomesh Arrays

ADVANCED MATERIALS 2025年第15期37卷 e2419653页

作者： Liao, Jiachi Shao, He Zhang, Yuxuan Yan, Yan Zeng, Ji Lan, Changyong Gao, Boxiang Chen, Dong Quan, Quan Xie, Pengshan Meng, You Ho, Johnny C. City Univ Hong Kong Dept Mat Sci & Engn Hong Kong 999077 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China Hunan Univ Changsha Semicond Technol & Applicat Innovat Res I Coll Semicond Coll Integrated Circuits Changsha 410082 Peoples R China Kyushu Univ Inst Mat Chem & Engn Fukuoka 8168580 Japan City Univ Hong Kong State Key Lab Terahertz & Millimeter Waves Hong Kong 999077 Peoples R China

The inherent limitations of traditional von Neumann architectures hinder the rapid development of internet of things technologies. Beyond conventional, complementary metal-oxide-semiconductor technologies, imaging sensors integrated with near- or in-sensor computing architectures emerge as a promising solution. In this study, the multi-scale van der Waals (vdWs) interactions in 1D tellurium (Te) atomic chains are explored, leading to the deposition of a photothermoelectric (PTE) Te nanomesh on a polymeric polyimide substrate. The self-welding process enables the lateral vapor growth of a well-connected Te nanomesh with robust electrical and mechanical properties, including a PTE responsivity of approximate to 120 V W-1 in the infrared light regime. Leveraging the PTE operation, the thermal-coupled bi-directional photoresponse is investigated to demonstrate a proof-of-principle in-sensor convolutional network for edge computing. This work presents a scalable approach for assembling functional vdWs Te nanomesh and highlights its potential applications in PTE image sensing and convolutional processing.

关键词： convolutional image processing in-sensor computing photothermoelectric detectors tellurium nanomesh

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Programmable Logic Functions-Integrated Acoustic in-sensor computing

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ADVANCED FUNCTIONAL MATERIALS 2025年第0期

作者： Zhang, Liang Tan, Ting Chen, Yinghua Yan, Zhimiao Shanghai Jiao Tong Univ Sch Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Sch Mech Engn Shanghai 200240 Peoples R China

in-sensor computing supports edge computing by reducing data transmission, but logic and arithmetic operations are still underdeveloped in acoustic sensors. Mechanical computing with metamaterials integrates these functions directly into sensors by responding to external stimuli, offering a promising solution. However, current mechanical logic switching depends on structural transformations, limiting logic function density. Therefore, a reprogrammable logic method is proposed using a geometrically imbalanced graded phononic crystal (GiGPnC). By designing graded unit cells, the structure produces two types of asymmetric scattering effects on antisymmetric Lamb waves, and creating constructive and destructive interference at the point defect. These four acoustic frequency responses correspond to all input-output mappings of a two-input one-output system, enabling mechanical computing. Then, reprogrammable realizations of seven basic logic gates and combinational logic are experimentally demonstrated, including a 1-bit half-subtractor and a 4-bit even parity generator, on a single GiGPnC. This frequency-response-based reprogrammable method can be extended to more complex logic functions. This reprogrammable design paradigm is expected for acoustic in-sensor computing centered on mechanical computing can promote the development of edge computing and Internet of Things (IoT).

关键词： Acoustic metamaterials asymmetric transmission in-sensor computing mechanical computing Phononic crystals

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Enhanced in-sensor computing with Spike Number-Dependent Plasticity Characteristics in an InGaSnO Optical Neuromorphic Device for Accelerating Machine Vision

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ACS NANO 2025年第13期19卷 13107-13117页

作者： Park, Min Ho Kim, Yeojin Choi, Min Jung Kim, Yu Bin Yun, Jung Min Jeong, Jun Hyung Kim, Seunghwan Park, Soohyung Kang, Seong Jun Kyung Hee Univ Dept Adv Mat Engn Informat & Elect Yongin 17104 South Korea Kyung Hee Univ Integrated Educ Program Frontier Mat BK21 Four Yongin 17104 South Korea Hyundai Mobis Tech Res Inst R&D Management Team DFX Grp Yongin 16891 South Korea Korea Inst Sci & Technol Adv Anal Ctr Seoul 02792 South Korea Yonsei Univ Dept Phys Seoul 03722 South Korea Univ Sci & Technol UST KIST Sch Div Nano & Informat Technol Seoul 02792 South Korea

in-sensor computing systems based on optical neuromorphic devices have attracted increasing attention to improve the efficiency and accuracy of machine vision systems. However, most materials used in optical neuromorphic devices exhibit spike timing-dependent plasticity (STDP) behavior in response to input light signals, leading to complex in-sensor computing and reduced machine vision accuracy. To address this issue, we introduce an indium gallium tin oxide (IGTO) semiconductor designed to enhance spike number-dependent plasticity (SNDP) in response to input light signals while eliminating the STDP behavior. Here, an IGTO-based optical neuromorphic device shows enhanced SNDP characteristics, which are attributed to the strong Sn-O bonding, as verified by photoemission spectroscopy (PES) analysis. The IGTO-based device consistently reaches the same conduction state after 8 light pulses regardless of the pulse timing and also achieves a conduction state based on the number of input light pulses even when 15 different pulse sets are applied. These results exhibit the SNDP characteristics of the IGTO-based device. Notably, in-sensor computing with the SNDP-enhanced device reduces multilayer perceptron (MLP) training time by 87.7% while achieving high classification accuracy. This study demonstrates that in-sensor computing systems with SNDP characteristics in optical neuromorphic devices have significant potential to accelerate machine learning for highly efficient machine vision systems.

关键词： in-sensor computing neuromorphic optoelectronic oxide machine learning

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Dual-Mode ZnO/SnSe Heterojunction Devices with Integrated Bipolar Response Photodetectors and Artificial Optoelectronic Synapses for in-sensor computing

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SMALL METHODS 2025年第0期 e2402151页

作者： Guo, Fuhai Yu, Weizhuo Zhang, Mingcong Zhang, Bo Hu, Bing Li, Shuangshuang Jiang, Jianyu Sun, Ankai Liu, Yunjie Hao, Lanzhong China Univ Petr Coll Sci Qingdao 266580 Shandong Peoples R China China Univ Petr Sch Mat Sci & Engn Qingdao 266580 Shandong Peoples R China

Optoelectronic synapse devices (OESDs) inspired by human visual systems enable to integration of light sensing, memory, and computing functions, greatly promoting the development of in-sensor computing techniques. Herein, dual-mode integration of bipolar response photodetectors (PDs) and artificial optoelectronic synapses based on ZnO/SnSe heterojunctions are presented. The function of the fabricated device can be converted between the PDs and OESDs by modulating the light intensity. As a PD, the polarity of the output current can be switched by tuning the laser wavelength and intensity, which is attributed to the competition between the photovoltaic and photothermoelectric effects in the ZnO/SnSe heterojunction. As an OESD, the device exhibits versatile photonic synaptic characteristics at low light intensity based on the defect-dominant carrier trapping/de-trapping processes, including short/long-term plasticity and learning experience behaviors. More importantly, benefitting from the outstanding synaptic responses, logic functions including "AND" and "OR" are implemented through the in-sensor computing architecture. This work supplies a novel route to realize complex functionality in one device and offers effective strategies for developing in-sensor computing.

关键词： artificial optoelectronic synapses bipolar photoresponse heterojunction in-sensor computing photodetectors

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in-sensor computing with Visual-Tactile Perception Enabled by Mechano-Optical Artificial Synapse

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ADVANCED MATERIALS 2025年第14期37卷 e2419405页

作者： Guo, Jiaxing Guo, Feng Zhao, Huijun Yang, Hang Du, Xiaona Fan, Fei Liu, Weiwei Zhang, Yang Tu, Dong Hao, Jianhua Nankai Univ Inst Modern Opt Tianjin 300071 Peoples R China Nankai Univ Tianjin Key Lab Microscale Opt Informat Sci & Tech Tianjin 300071 Peoples R China Hong Kong Polytech Univ Dept Appl Phys Hong Kong 999077 Peoples R China China Univ Geosci Fac Mat Sci & Chem 388 Lumo Rd Wuhan 430074 Peoples R China Nankai Univ Inst Photoelect Thin Film Devices & Technol Coll Elect Informat & Opt Engn Tianjin 300071 Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

in-sensor computing paradigm holds the promise of realizing rapid and low-power signal processing. Constructing crossmodal in-sensor computing systems to emulate human sensory and recognition capabilities has been a persistent pursuit for developing humanoid robotics. Here, an artificial mechano-optical synapse is reported to implement in-sensor dynamic computing with visual-tactile perception. By employing mechanoluminescence (ML) material, direct conversion of the mechanical signals into light emission is achieved and the light is transported to an adjacent photostimulated luminescence (PSL) layer without pre- and post-irradiation. The PSL layer acts as a photon reservoir as well as a processing unit for achieving in-memory computing. The approach based on ML coupled with PSL material is different from traditional circuit-constrained methods, enabling remote operation and easy accessibility. Individual and synergistic plasticity are elaborately investigated under force and light pulses, including paired-pulse facilitation, learning behavior, and short-term and long-term memory. A multisensory neural network is built for processing the obtained handwritten patterns with a tablet consisting of the device, achieving a recognition accuracy of up to 92.5%. Moreover, material identification has been explored based on visual-tactile sensing, with an accuracy rate of 98.6%. This work provides a promising strategy to construct in-sensor computing systems with crossmodal integration and recognition.

关键词： in-sensor computing Mechanoluminescence Mechano-optical artificial synapse Photostimulated luminescence Visual-tactile perception

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Supporting in-sensor computing with Hardware-Aware Neural Architecture Search 55th

Supporting in-Sensor Computing with Hardware-Aware Neural Ar...

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55th Meeting of the Italian Electronics Society

作者： Garavagno, Andrea Mattia Ragusa, Edoardo Zunino, Rodolfo Frisoli, Antonio Gastaldo, Paolo Univ Genoa Dept Elect Elect Telecommun Engn Naval Architecture DITEN I-16126 Genoa GE Italy Scuola Super Sant Anna Dept Excellence Robot & AI Piazza Martiri Liberta 33 I-56127 Pisa Italy Scuola Super Sant Anna Inst Mech Intelligence I-56010 Pisa Italy

ISBN: (纸本)9783031715204;9783031715181;9783031715174

Hardware-aware neural architecture search (HW-NAS) is a powerful tool for the automatic design of tiny neural architectures able to fit the tight memory constraints of commodity electronics, like microcontroller units (MCU). However, new sensors equipped with neural processing units (NPUs) are reaching the market. In contrast to common MCUs, NPUs often do not feature the traditional division of the memory constraint between RAM and Flash usage. This is an issue for state-of-the-art HW-NAS, which are designed to fit the two constraints distinctly. This work proposes a novel HW-NAS for designing tiny Convolutional Neural Networks (CNNs) that can effectively deal with NPUs, achieving state-of-the-art results in just 5 h and 48 min on the Visual Wake Words dataset, a tinyML benchmark.

关键词： in-sensor computing hardware-aware neural architecture search tinyML

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Haptic in-sensor computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2025年第3期7卷

作者： Kimizuka, Kouki Azhari, Saman Tokuno, Shoshi Karacali, Ahmet Usami, Yuki Ikemoto, Shuhei Tamukoh, Hakaru Tanaka, Hirofumi Kyushu Inst Technol Grad Sch Life Sci & Syst Engn 2-4 Hibikino Aizu Wakamatsu Kitakyushu 8080196 Japan Waseda Univ Grad Sch Informat Prod & Syst IPS 2-7 Hibikino Aizu Wakamatsu Kitakyushu 8080135 Japan Kyushu Inst Technol Res Ctr Neuromorph AI Hardware 2-4 Hibikino Aizu Wakamatsu Kitakyushu 8080196 Japan

The importance of haptic in-sensor computing devices has been increasing. Herein, a haptic sensor with a hierarchical structure is successfully fabricated via the sacrificial template method, using carbon nanotube-polydimethylsiloxane (CNT-PDMS) nanocomposites for in-sensor computing applications. The CNT-PDMS nanocomposite sensors, with different sensitivities, are obtained by varying the amount of CNTs. The input stimuli are transformed into higher-dimensional information, enabling a new path for the CNT-PDMS nanocomposite application, which is implemented on a robotic hand as an in-sensor computing device by applying a reservoir computing paradigm. The nonlinear output data obtained from the sensors are trained using linear regression and used to classify nine different objects used in everyday life with an object recognition accuracy of >80% for each object. This approach can enable tactile sensation in robots while reducing the computational cost.

关键词： haptic sensors in-sensor computing nanomaterials reservoir computing soft robotics

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Flexible Neuromorphic Electronics for Wearable Near-sensor and in-sensor computing Systems

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ADVANCED MATERIALS 2025年第9期37卷 e2416073页

作者： Jang, Hyowon Lee, Jihwan Beak, Chang-Jae Biswas, Swarup Lee, Sin-Hyung Kim, Hyeok Univ Seoul Ctr Smart Sensor Syst Seoul CS4 Sch Elect & Comp Engn 163 Seoulsiripdaero Seoul 02504 South Korea Univ Seoul Sch Adv Fus Studies Dept Intelligent Semicond Engn 163 Seoulsiripdaero Seoul 02504 South Korea

Flexible neuromorphic architectures that emulate biological cognitive systems hold great promise for smart wearable electronics. To realize neuro-inspired sensing and computing electronics, artificial sensory neurons that detect and process external stimuli must be integrated with central nervous systems capable of parallel computation. In near-sensor computing, synaptic devices, and sensors are used to emulate sensory neurons and receptors, respectively. In contrast, in in-sensor computing, a single multifunctional device serves as both the receptor and neuron. Bio-inspired cognitive systems efficiently detect and process stimuli through data structuring techniques, significantly reducing data volume and enabling the extension of neuromorphic applications to smart wearable systems. To construct wearable near- and in-sensor computing, it is crucial to develop artificial sensory neurons and central nervous synapses that replicate the biological functionalities. Additionally, the integrated systems must exhibit high mechanical flexibility and integration density. This review addresses research on flexible bio-inspired cognitive systems, classified into near- and in-sensor computing. It covers fundamental aspects, including biological cognitive processes, the required components, and the structures for each component, as well as applications for wearable smart systems. Finally, it offers perspectives on future research directions for flexible neuromorphic electronics in smart wearable systems connected to the next-generation Internet of Things.

关键词： flexible synaptic device in-sensor computing near-sensor computing sensory neuron smart wearable electronics

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Adaptive in-sensor computing for Enhanced Feature Perception and Broadband Image Restoration

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ADVANCED MATERIALS 2025年第6期37卷 e2414261页

作者： Shao, He Wang, Weijun Zhang, Yuxuan Gao, Boxiang Jiang, Chunsheng Li, Yezhan Xie, Pengshan Yan, Yan Shen, Yi Wu, Zenghui Wang, Ruiheng Ji, Yu Ling, Haifeng Huang, Wei Ho, Johnny C. City Univ Hong Kong Dept Mat Sci & Engn Hong Kong 999077 Peoples R China Nanjing Univ Posts & Telecommun NJUPT State Key Lab Organ Elect & Informat Displays Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun NJUPT Inst Adv Mat IAM Nanjing 210023 Peoples R China City Univ Hong Kong State Key Lab Terahertz & Millimeter Waves Hong Kong 999077 Peoples R China Kyushu Univ Inst Mat Chem & Engn Fukuoka 8168580 Japan

Traditional imaging systems struggle in weak or complex lighting environments due to their fixed spectral responses, resulting in spectral mismatches and degraded image quality. To address these challenges, a bioinspired adaptive broadband image sensor is developed. This innovative sensor leverages a meticulously designed type-I heterojunction alignment of 0D perovskite quantum dots (PQDs) and 2D black phosphorus (BP). This configuration enables efficient carrier injection control and advanced computing capabilities within an integrated phototransistor array. The sensor's unique responses to both visible and infrared (IR) light facilitate selective enhancement and precise feature extraction under varying lighting conditions. Furthermore, it supports real-time convolution and image restoration within a convolutional autoencoder (CAE) network, effectively countering image degradation by capturing spectral features. Remarkably, the hardware responsivity weights perform comparably to software-trained weights, achieving an image restoration accuracy of over 85%. This approach offers a robust and versatile solution for machine vision applications that demand precise and adaptive imaging in dynamic lighting environments.

关键词： Bioinspired sensor broadband image restoration feature extraction in-sensor computing phototransistor array

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Emerging 2D Ferroelectric Devices for In-sensor and In-Memory computing

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ADVANCED MATERIALS 2025年第2期37卷 e2400332页

作者： Chen, Chunsheng Zhou, Yaoqiang Tong, Lei Pang, Yue Xu, Jianbin Chinese Univ Hong Kong Dept Elect Engn & Mat Sci Hong Kong Peoples R China Chinese Univ Hong Kong Technol Res Ctr Hong Kong Peoples R China

The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics. This work reviews the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices are reviewed followed by the integration of perception, memory, and computing application. Notably, the 2D ferroelectric devices have been used to simulate synaptic weights, neuronal mod

关键词： 2D materials ferroelectric device in-memory computing in-sensor computing neural network

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