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Nano high-entropy oxide cathode with enhanced stability for direct borohydride fuel cells

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Journal of Energy Chemistry 2025年第1期100卷 309-316页

作者： Lei Zhang Lingfeng Kuang Lianke Zhang Wen Chu Haiying Qin Jing Zhang Junjing He Hualiang Ni Hongzhong Chi New Energy Materials Research Center College of Materials and Environmental Engineering Hangzhou Dianzi University School of Materials Science and Engineering Zhejiang University key laboratory of MEMS of Ministry of Education School of Integrated Circuits Southeast University

High-entropy materials have become high-activity electrocatalysis owing to their high-entropy effect and multiple active ***,we synthesize a series of carbon-supported nano high-entropy oxides（HEOs/C）,specifically （PtFeCoNiCu）O/C,using a carbothermal shock （CTS） method for application as a cathode catalyst in direct borohydride fuel cells （DBFCs）.The microstructure of the prepared catalysts was characterized by X-ray photoelectron spectroscopy,X-ray absorption fine structure,and transmission electron *** prepared （PtFeCoNiCu）O/C,with particle sizes ranging from 2 to 4 nm,demonstrates 3.94 transferred electrons towards the oxygen reduction reaction in an alkaline environment,resulting in a minimal H2O2yield of 2.6%.Additionally,it exhibits a Tafel slope of 61 mV dec-1,surpassing that of commercial Pt/C (82 mV dec-1).Furthermore,after 40,000 cycles of cyclic voltammetry（CV） testing,the half-wave potential of （PtFeCoNiCu）O/C shows a positive shift of 3 mV,with no notable decline in the limiting current *** （PtFeCoNiCu）O/C is used as a cathode catalyst in DBFCs,the DBFC achieves a maximum power density of 441 mW cm-2at 60°C and sustains a cell voltage of approximately 0.73 V after 52 h at 30°*** findings confirm that HEO/C is a promising cathode catalyst for DBFCs.

关键词： Direct borohydride fuel cells (DBFC)

Mitigating methodology of hardware non-ideal characteristics for non-volatile memory based neural networks

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Science China(Information Sciences) 2025年

作者： Lixia HAN Peng HUANG Yijiao WANG Zheng ZHOU Haozhang YANG Yiyang CHEN Xiaoyan LIU Jinfeng KANG School of integrated circuits Peking University Beijing Advanced Innovation Center for integrated circuits School of integrated Circuit Science and Engineering Beihang University

Non-volatile memory-based computing-in-memory (nvCIM) paradigm has been extensively studied to boost the energy efficiency of neural network accelerators in edge ***,the degradation of inference accuracy induced by the non-ideal characteristics across circuits,arrays,and devices is becoming a crucial *** this work,we establish a hardware characteristic behavior model to analyze the impact of nvCIM non-ideal characteristics on neural network *** we propose a hardware aware training and weight mapping correction methods to mitigate inference accuracy *** simulation verification,about 95%inference accuracy degradation is recovered by adopting the proposed mitigation method for various non-ideal characteristics and various neural network *** feasibility of the proposed method is further proved in an experimental example with a flash-based LeNet recognition system.

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High-performance transparent graphene-based antennas

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Science China Technological Sciences 2025年

作者： Chaoye HONG Lusong WEI Weijia WANG Youmei TANG Qinghai ZHU Jiangtao HUANGFU Mingsheng XU College of integrated circuits State Key Laboratory of Silicon and Advanced Semiconductor Materials Zhejiang University College of Information Science and Electronic Engineering Zhejiang University

With the continuous development of wireless communication technology, applications in the military, aerospace,and vehicle wireless communication sectors have demanded miniaturized and aesthetically pleasing antennas. Transparent antennas, which combine the advantages of optical and radio-frequency(RF) technologies, have become a popular research topic. However, existing transparent antennas often suffer from complex preparation processes, fragile materials, poor conductivity, and low optical transmittance. Here, we develop hybrid conductive thin films using graphene and silver nanowires,achieving a transmittance of 86% and a sheet resistance of 7.8 Ω/sq with high environmental stability. We demonstrate the suitability of this nanocomposite in two applications: a transparent ultrahigh-frequency RF-identification tag folded dipole antenna and a transparent X-band microwave half-wave dipole antenna. These antennas offer high gain, high radiation efficiency, and small size, indicating that transparent graphene-based antennas have remarkable potential for intelligent information sensor systems.

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Smaller, faster, lower-power analog RRAM matrix computing circuits without performance compromise

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Science China(Information Sciences) 2025年

作者： Yubiao LUO Pushen ZUO Shiqing WANG Zhong SUN Ru HUANG School of integrated circuits Peking University Institute for Artificial Intelligence Peking University Beijing Advanced Innovation Center for integrated circuits

Recently, the analog matrix computing (AMC) concept has been proposed for fast, efficient matrix operations, by configuring global feedback loops with crosspoint resistive memory arrays and operational amplifiers (OAs). The implementation of a general real-valued matrix (containing both positive and negative elements) is enabled by using a set of analog inverters,which, however, is considered inefficient regarding circuit compactness, power consumption, and temporal response. Here, with the assistance of the conductance compensation (CC) strategy to take full advantage of the inherent differential inputs of OAs,new AMC circuits without analog inverters are designed. Such a design saves the area occupation and power dissipation of analog inverters, and thus turns to be smaller and lower-power. Simulation results reveal that the new circuit also shows a faster response towards the steady state, thanks to the reduction of poles in the circuit, which, again, is contributed by the elimination of analog inverters. Along with all of these benefits, extensive simulations demonstrate that the CC-AMC circuits do not compromise the computing performance in terms of relative error caused by various non-ideal factors in the circuit.

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An AND-type 1T-FeFET array with robust write and read operations

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Science China(Information Sciences) 2025年

作者： Jiacheng XU Jiayi ZHAO Hongrui ZHANG Haoji QIAN Jiani GU Bowen CHEN Gaobo LIN Rongzong SHEN Xinda SONG Huan LIU Yian DING Minglei MA Miaomiao ZHANG Xiao YU Bing CHEN Ran CHENG Gaobo XU Huaxiang YIN Yan LIU Jiajia CHEN Chengji JIN Genquan HAN Hangzhou Institute of Technology and School of Microelectronics Xidian University Zhejiang Lab College of integrated circuits Zhejiang University key laboratory of Microelectronics Devices and integrated Technology Institute of MicroelectronicsChinese Academy of Sciences

HfO2-based ferroelectric field-effect transistor （FeF ET） has become a promising solution for next-generation embedded non-volatile memory （NVM） owing to its complementary metal-oxide-semiconductor （CMOS） compatibility,small footprint, high performance, and non-destructive readout [1]. Recent research of HfO2-based FeF ET has focused on the device operation mechanisms as well as reliability issues such as the interplay between polarization switching and charge trapping/detrapping [2], read-after-write latency [3] and endurance degradation [4]. It has been demonstrated that endurance higher than 1010cycles with negligible read-after-write latency is possible if the gate stack of FeF ET is well engineered [5].

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A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

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Nano-Micro Letters 2025年第2期17卷 417-431页

作者： Yunjian Guo Kunpeng Li Wei Yue Nam‑Young Kim Yang Li Guozhen Shen Jong‑Chul Lee Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897South Korea Radio Frequency integrated Circuit(RFIC)Bio Centre Kwangwoon UniversitySeoul 01897South Korea Department of Electronic Engineering Kwangwoon UniversitySeoul 01897South Korea School of Microelectronics Shandong UniversityJinan 250101People’s Republic of China State key laboratory of integrated Chips and systems Fudan UniversityShanghai 200433People’s Republic of China School of integrated circuits and Electronics Beijing Institute of TechnologyBeijing 100081People’s Republic of China

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily *** existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple *** features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance *** wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist ***,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various *** system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and *** proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific *** utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

关键词： Wearable wristband Self-supervised contrastive learning Dynamic gesture Air-writing Human-machine interaction

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Software-defined process-near-memory architecture using 3D hybrid bonding integration

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Science China(Information Sciences) 2025年第1期68卷 366-378页

作者： Anlin XU Chenchen DENG Jianfeng ZHU Yao WANG Shaojun WEI Leibo LIU School of integrated circuits Tsinghua University Beijing Institution of Tracking and Communication Technology Beijing National Research Center for Information Science and Technology Tsinghua University

With the unprecedented explosive growing amount of global data, the development of computing chips, which encounter bottlenecks such as power wall and memory wall, cannot satisfy the demanding requirement. This work proposes a software-defined process-near-memory(SDPNM) computing architecture implemented using 3D hybrid bonding *** software-defined chip architecture, featuring spatial computations and dynamic reconfiguration, innovates in a top-down manner to achieve high energy efficiency while maintaining flexibility after fabrication. The process-near-memory integration further advances the SDNPM chip in a bottom-up way to reduce the energy consumption of data movement while improving the bandwidth. Utilizing a relatively mature fabrication and bonding process can result in feasible solutions for both data-intensive and compute-intensive applications including digital signal processing and artificial intelligence. The logic die is fabricated in the SMIC 40 nm process and the DRAM die is fabricated in the PSMC 25 nm process. The hybrid bonding is implemented by XMC. The experimental results show that the energy efficiency of the proposed SDPNM chip is 33.1× better than the state-of-the-art FPGA ranging from 8.2× to 104.1×.

关键词： software-defined chips process near memory energy efficiency dynamic reconfiguration domain specific

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An NIR-Ⅱ-emissive Ru(Ⅱ) metallacycle for efficient and safe immunotherapy

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Science China Chemistry 2025年第1期68卷 5-7页

作者： Chonglu Li Jianbo Zhan Biao Dong Institute of Health Inspection and Testing Hubei Provincial Center for Disease Control and Prevention State key laboratory on integrated Optoelectronics College of Electronic Science and Engineering Jilin University

Immunotherapy, as a groundbreaking cancer treatment, has gained considerable attention for its utilization of the body’s immune system to combat tumors [1]. This therapy induces tumor cell apoptosis, triggering a sustained anti-tumor immune response and effectively preventing tumor recurrence and metastasis [2]. The fundamental principle lies in inducing tumor cell death through specific treatment modalities, leading to the release of a cascade of signaling molecules and antigens, thereby mobilizing the immune system to actively identify and attack tumor cells [3].

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Polarization-encoded neural networks with simplified grating patch

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Science China Technological Sciences 2025年

作者： Chengyan ZHONG Xiang WANG Lingfei LI Yuanchi CUI Lei XIAO Dawei SONG Junxiong GUO Wen HUANG Yufeng GUO Yu LIU College of integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications National and Local Joint Engineering laboratory of RF Integration and Micro-Assembly Technology College of integrated circuits Hangzhou Global Scientific and Technological Innovation Centre Zhejiang University Nanjing Mumusili Technology Co. LTD School of integrated circuits Tsinghua University Institute of Advanced Study School of Electronic Information and Electrical Engineering Chengdu University State key laboratory of Electronic Thin Films and integrated Devices School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of China

Optical neural networks (ONNs) offer a promising solution for high-performance, energy-efficient artificial intelligence hardware by leveraging the parallelism and speed of light. However, the large-scale implementation of ONNs remains challenging due to the bulky footprint and complex control of optical synapses. In this work, we propose and simulate a plasmonic polarized synaptic architecture that overcomes the diffraction limit and enables ultra-compact ONNs. By tuning the polarization state of incident light, the optical transmittance through each plasmonic unit can be dynamically adjusted to represent a synaptic weight. Our plasmonic structures, with features as small as 40 nm, operate well below this limit in the visible spectrum (400–750 nm). Compared with diffraction and interference-based circuit designs, our proposed method achieves a substantial reduction in synaptic density by factors of 150000-fold and 1500-fold, respectively. Furthermore, we successfully demonstrate a proof-of-concept plasmonic ONN applied to the Canadian Institute for Advanced Research—10classes (CIFAR-10) dataset using a Visual Geometry Group network with 16 layers (VGG16) model. After training for 80epochs, the network achieves an accuracy of 93%. The polarization-tunable plasmonics paves the way towards scalable ONNs for next-generation artificial intelligence (AI) accelerators and smart sensors.

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