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Jing Zhang is currently a Professor at School of Automation Science and Engineering, Xi’an Jiaotong University. He obtained bachelor's and doctoral degrees from the Department of Mathematics and the Department of Automation at Tsinghua University in 2001 and 2006, respectively. He worked as a Postdoc at Tsinghua University from 2006 to 2008, and then worked at Department of Automation, Tsinghua University from 2008 to 2022. In 2022, he joined Xi’an Jiaotong University and worked as a Second-Class Professor. Professor Zhang mainly engaged in research on quantum and micro nano system control, optical microresonator sensors, etc. He has published more than 80 papers in authoritative journals in the fields of physics and control, e.g., IEEE Transactions on Automatic Control, Nature, Nature Photonics, Physics Reports, Physical Review Letters, Nature Communications. His work on chaotic propagation and stochastic resonance in optomechanical systems was selected as the cover hot paper of the June 2016 issue by Nature Photonics and was awarded as the "2016 Important Achievements in Chinese Optics". As the first author, his work has be awarded by the International Federation of Automatic Control (IFAC) as Young Author Award, which is one of the most important international awards in the field of Automation. He received support from the National Natural Science Foundation of China's Outstanding Youth Fund in 2016, and then selected as a Young Changjiang Scholar in 2017. In 2021, he was selected as a Changjiang Distinguished Professor.

Speech Title: Optical sensor based on chip-integrated optical microresonator

Abstract: In recent years, with the development of cutting-edge technology, sensing techniques have approached the classical noise limit. Optical sensing technique based on optical microresonators, leveraging resonance enhancement and noise suppression technologies, holds the potential to break this limit while combining precision and size advantages. Additionally, optical microresonator sensors can achieve on-chip integration through semiconductor silicon-based fabrication techniques. The report introduces the development and applications of chip-integrated optical microresonator sensors. 1) First, we introduce the fundamental principles, competitive advantages, and our progresses of optical microresonator sensors. 2) Next, we elaborate on three application scenarios of optical microresonator sensors in underwater target detection and autonomous navigation: i) The application of optical microresonator sensors in autonomous navigation, specifically for gyroscopes and accelerometers; ii) The application of optical microcavity sensors in underwater acoustic detection, namely hydrophones. We detail their detection principles, results from pool calibration, lake trials, and sea tests, with calibration results surpassing traditional piezoelectric hydrophones by 1-2 orders of magnitude; iii) The application of optical microresonator sensors in underwater target magnetic detection, with a detection accuracy of 0.19pT/Hz^1/2, and verification experiments using simulated radar scanning are provided. 3) A prospect was made for the application of optical microresonator sensors in quantum sensing.

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Prof Jin Xie received B.Eng. degree from Tsinghua University in 2000, and M.Eng. degree from Zhejiang University in 2003, and the Ph.D. degree from Nanyang Technological University, Singapore in 2008. From 2007 to 2011, he worked in Institute of Microelectronics, Singapore, where he was a scientist in the Sensors and Actuators Microsystems Program. From June 2011 to July 2012, he worked as a post-doc researcher in the Department of Mechanical Engineering, University of California, Berkeley, CA, USA. He was a research member of Berkeley Sensor & Actuator Center (BSAC). In October 2012, he joined School of Mechanical Engineering, Zhejiang University as a full professor. His research interests include MEMS sensors and actuators, wearable devices and flexible sensors. He has published more than 120 journal papers and 80 conference papers, and has been granted 15 Chinese invention patents. He was TPC member of IEEE NEMS 2015, 2018, 2020, 2022 and Transducers 2021. He is an IEEE member, and is an editorial board member of the journal of AI Sensors (MPDI) and the journal of Smart Wearable Technology.

Speech Title: Human-Machine Interaction Based on Piezoelectric Micromachined Ultrasonic Transducers

Abstract: Wearable ultrasonic devices can non-invasively monitor muscle activities for prosthetic hand control and human-machine interaction (HMI). However, most existing ultrasonic probes for deep muscle tissue monitoring are designed with bulky structures, limiting their wearability for long-term continuous monitoring. This talk includes three topics: (1) First, a sensing and controlling strategy is introduced for upper extremity prosthetics through finger motion decoding based on wearable piezoelectric micromachined ultrasound transducer (PMUT). (2) Secondly, a strategy is proposed for simultaneous gesture recognition and upper limb angle measurements using multiple A-mode ultrasonic patches, enabling real-time control of a four-degree-of-freedom (4-DOF) virtual robotic arm. (3) Finally, we present a soft electronic skin (E-skin) for 3D hand reconstruction inspired by human biology, which integrates a multi-sensor fusion of stretchable carbon nanotubes (CNTs) strain sensors and PMUT.

Weixin Liu is a Postdoctoral Research Fellow in the Department of Electrical and Computer Engineering at the National University of Singapore. His research spans nano-opto-electro-mechanical (NOEM) systems, silicon photonics, mid-infrared waveguide sensing, and microwave photonics. He received the B.Eng. in Mechatronics Engineering from Zhejiang University in 2019 and the Ph.D. in Electrical and Computer Engineering from NUS in 2024. Dr. Liu’s first-author publications include Science, Nano Letters, Advanced Optical Materials, and Nanophotonics.

Speech Title: Nano-opto-electro-mechanical gratings for on-chip light switching and spectral shaping

Abstract: Programmable spectral shaping, i.e., dynamic and arbitrary control over spectra, promises to reshape photonic systems with unprecedented spectral efficiency and network agility. However, most existing photonic components offer rigid, weakly tunable spectral functions, constraining compact and high-speed on-chip implementations. This talk presents a pixelated nano-opto-electro-mechanical (NOEM) grating that leverages electromechanically induced symmetry breaking to achieve pixel-level, precise control of the grating coupling strength, enabling an ultracompact on-chip spectral shaper. The device combines a high degree of spectral programmability with fast, reconfigurable operation on a single chip, opening new routes toward elastic bandwidth allocation in optical networks and frequency-domain parallel optical signal processing. It also points to opportunities in optical computing, microwave photonics, and quantum photonics.

Zhiwu Han, professor and doctoral supervisor, is currently the director of Key Laboratory of Bionic Engineering (Ministry of Education) of Jilin University. His research encompasses the study in mechanical bionics, bionic perception theory and technology, bionic functional surface and advanced biomimetic composites. His work aims to apply mechanical bionic principles to hypersensitive sensors, lightweight high-strength materials, and micro-nano manufacturing. He serves as a fellow of the International Society of Bionic Engineering, the associate editor of Journal of Bionics Engineering, and vice chairman of the editorial board of Chinese Journal of Mechanical Engineering. To date, Prof. Han has authored more than 100 peer-reviewed papers in notable journals, including Nature, Science Advances, Advanced Materials, Progress in Materials Science, and Materials Today. His pioneering work in mechanical bionics has won more than 5 significant awards at the national or provincial level, such as Second Prize of the State Technological Innovation Award, First Prize of the Ministry of Education Technology Invention Award, First Prize of the Jilin Province Technology Invention Award, First Prize of the Mechanical Industry Science and Technology Award, etc.

Speech Title: Bionic high-performance perception theory and technology

Abstract: Sensors, which serve as crucial instruments for gathering external information, have drawn substantial attention in domains such as electronic skin, human-computer interaction, and precision instrumentation. Among these, mechanical sensors represent a major area of research focus. However, the performance of existing mechanical sensors in terms of weak vibration detection, target signal identification, and vibration source localization is difficult to meet the practical requirements. Therefore, it is important to find innovative methods to design high-performance mechanical sensors. Over billions of years of evolution, the fusion of organisms and nature has become perfect. This report takes the scorpion as its biological prototype. Through systematic bionic studies, a range of intrinsic mechanisms underlying the scorpion's remarkable perceptual functions have been revealed. These principles enable scorpions to detect faint stimuli, accurately discriminate signals, and quickly locate vibration sources even in complex settings, which is conducive to avoiding enemies and hunting for food in harsh conditions to maintain survival. Drawing inspiration from such ingenious biological strategies, corresponding designs and fabrication methods for biomimetic sensors have been developed and widely applied in fields such as automotive industry manufacturing, micro-vibration monitoring of bridges, and fault monitoring of precision equipment.

Dr. Jian-Hao Chen is currently a Professor at the School of Physics, Peking University. He received his PhD in Physics from the University of Maryland in 2009. After graduation, he worked as a post-doctoral researcher at the University of Maryland and the University of California, Berkeley. He joined ICQM, Peking University as a tenure-track Associate Professor in 2013, and then promoted to tenured Associate Professor in 2019, and to tenured Full Professor in 2024. His research interests focus on quantum transport and device physics of low-dimensional materials, including topological and correlated materials, magnetic materials and high mobility materials as well as their heterostructures. He has published more than 60 papers with more than 12,000 citations (Google Scholar). He has also been listed as the most cited Chinese Researchers for the year of four consecutive years from 2021 to 2024 by Elsevier. He is currently the Physics Editor of National Science Review and Associate Editor of npj 2d Materials and Applications.

Speech Title: Spin transport with magnon-flat bands and spin-spin correlations

Abstract: van der Waals magnets have been a fertile playground for low-dimensional spin excitation and spin-based device applications; spin-spin correlations in complex spin structures provide exotic correlated states that might facilitate spin excitation and transport. We will discuss our recent process in a long distance, quasi-1D magnon transport in a 2D spin lattice, aid by the formation of a flat magnon band; short introduction of an electrically operated digital magnon NOT gate[1] and an electrically operated magnon anisotropic read-only memory [2] will also be provided; lastly, we will discuss an ultra-long distance spin transport phenomena based on spin-spin correlation instead of conventional exchange interactions (Figure 1)[3]. These results unveil the potential of van der Waals anti-ferromagnets and complex magnetic materials for studying highly tunable spin physics and for application in magnon-base circuitry in future information technology. [1] Guangyi Chen et al., Nature Communications 12:6279 (2021) [2] Shaomian Qi et al., Nature Communications 14: 2526 (2023) [3] Di Chen et al., Nature Communications (2025) Online publication: https://www.nature.com/articles/s41467-025-68203-4

Xingsheng Wang obtained his Ph.D. from the University of Glasgow, UK, in 2010, later he worked in Synopsys as senior engineer and he joined Huazhong University of Science and Technology (HUST) as a full professor in 2018. Prof. Wang focuses on the research of Design–Technology Co-Optimization (DTCO) EDA and novel computing-in-memory chip technologies. He has published over a hundred papers in leading IEEE journals and conferences such as IEEE EDL, TED, ISCAS, DAC, ICCAD, and IRPS. He holds more than ten major research projects, including key projects from NSFC and National Key R&D Program, also joint research projects with YMTC, and Huawei. He serves as TPC sub-committee chairs or members of IEEE ISEDA and ICTA, DAC and ETDM. He is a senior member of IEEE, CCF, and CAAI. He received the 2022 Huawei OlympusMons Pioneer Award.

Speech Title: Memristor-based Computing-in-Memory Chip Co-Design for Edge AI

Abstract: Analog matrix multiplication based on memristor crossbar arrays can significantly increase computational throughput and energy efficiency. However, its physical implementation faces several fundamental challenges, including unclear device dynamics, reliability issues in multilevel operation, and IR-drop of the array. To address these issues, we fabricated CMOS-compatible memristor array chips based on 180 nm and 55 nm processes and developed a 3D multi-physics device simulator and crossbar array simulators. This enabled us to clarify the conductive filament formation and rupture mechanism in memristors, as well as the random telegraph signal (RTS) mechanism affecting read current reliability. We also developed superlattice-like memristors to achieve stable multilevel characteristics. For the critical issue of crossbar IR-drop, which severely impacts computational accuracy and throughput, we implemented a fast and accurate modeling method with O(n) complexity. Through co-optimization of layout and circuit design, along with a proposed real-time deployment correction approach, we substantially increased the scale of a single multiply-accumulate core to several megabits. These advances systematically overcome the bottleneck of array scaling, significantly improving both computational performance and energy efficiency of memristor computing-in-memory chips.

Baoyong Chi received the B.S. degree in microelectronics from Peking University, Beijing, China, in 1998, and the Ph.D. degree from Tsinghua University, Beijing, in 2003. From 2006 to 2007, he was a Visiting Assistant Professor with Stanford University, Stanford, CA, USA. He is currently a Full Professor at the School of Integrated Circuits, Tsinghua University, where he also serves as Chair of the Academic Degrees Sub-Committee. His primary research focuses on radio-frequency (RF)/millimeter-wave integrated circuits and RF microsystems for communication and radar. He has been recognized with several prestigious honors, including the National Science Fund for Distinguished Young Scholars from the NSFC and the High-Level Talents from the Ministry of Education. He has published over 200 academic papers. Prof. Chi has served as TPC Chair of IEEE A-SSCC, Guest Editor for IEEE Journal of Solid-State Circuits (JSSC) and IEEE Transactions on Circuits and Systems II (TCAS-II), and Editorial Board Member of SCIENCE CHINA Information Sciences. As principal investigator, he has led major projects from the NSFC, National High-Tech R&D Program (863 Program), National Science and Technology Major Project, and National Key R&D Program. His awards include the Tsinghua University Academic Newcomer Award, the China Institute of Electronics Outstanding Scientist Award, the Second Prize of Beijing Municipal Science and Technology Progress Award, and the Grand Prize of Tianjin Natural Science Award.

Speech Title: Accuracy-Boosted Temperature Compensated Crystal Oscillator

Abstract: Accurate megahertz clocks, typically generated by crystal oscillators (XOs), play a critical role in wireless communication systems. High frequency accuracy could be achieved by the active compensation in the temperature compensated crystal oscillator (TCXO). However, the spectral purity is usually degraded by the noise and distortion from the active compensation. This talk reports a megahertz TCXO featuring a time-domain accuracy boosting scheme to enhance the signal-to-noise and distortion ratio (SNDR) at the oscillator core’s tuning port. The key design considerations and circuit details are discussed. The TXCO is implemented in a 0.18-µm CMOS process, and achieves ±0.6 ppm/±1.4 ppm frequency accuracy over -55◦C to 85◦C/115◦C, corresponding to a 38:6×/13:2× improvement over uncompensated cases while maintaining competitive noise performance compared with existing solutions.

Professor Jinling Yang currently serves as a Professor and Doctoral Supervisor at the Institute of Semiconductors, Chinese Academy of Sciences (CAS), and is also a professor at the University of Chinese Academy of Sciences. She received her Ph.D. in Condensed Matter Physics from the Institute of Physics, CAS, in 1997. She subsequently conducted research at Tohoku University (Japan), the University of Freiburg (Germany), and the IBM Zurich Research Lab/University of Basel (Switzerland). In 2004, she was selected for the CAS "Hundred Talents Program" and returned to China to continue her work. Her research focuses on Micro-Electro-Mechanical Systems, particularly the development and application of resonators, oscillators, scanning probes, and so on. Professor Yang has published 2 scientific books and over 100 peer-reviewed papers, which have been cited more than 2,000 times in prestigious journals including Nature and its sister journals. She was invited to contribute chapters to the Encyclopedia of Nanotechnology and the Handbook of MEMS. She has authorized 1 PCT patent, 3 European patents, and over 60 Chinese invention patents. Her distinguished contributions have been recognized with awards including the Innovation Talent Award from the Chinese Overseas Community and the 2022 Hubei Provincial Second Prize for Scientific and Technological Progress. The scanning probe technology developed by her team was featured as a research highlight in Nature Methods.

Speech Title: Deep learning based inverse design for non-parameterized MEMS structural design

Abstract: In light of the growing demand for miniaturized, high-performance sensors in the Internet of Things, the optimal design of Micro-Electro-Mechanical System resonators has become a critical challenge. Conventional design methods heavily rely on finite element analysis and engineers' prior knowledge, resulting in prolonged design cycles, high computational costs, and difficulties in achieving co-optimization of multi-objective performances. This presentation introduces a revolutionary design paradigm developed to address these challenges—an inverse design framework for MEMS resonators based on deep learning and genetic algorithms. This framework can automatically generate high-performance 3D structures directly from target physical properties like frequency, quality factor, and motional impedance, without predefining the model topology. Beyond enabling rapid (within 10 minutes) and accurate (average deviation (3%) device design, this method also demonstrates exceptional versatility, supporting multiple vibration modes and a broad frequency range. Our research validates the algorithm's effectiveness via simulations and experiments, laying a solid foundation for its application in accelerating the innovative design of various MEMS devices.

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Speech Title: Optimization and Yield Analysis Method for MEMS Devices Based on Deep Neural Network Method

Abstract: The development of high-performance MEMS devices needs efficient design optimization and yield analysis in the presence of unavoidable process variations. The existing algorithms are difficult to integrate different requirements of optimization and uncertainty quantification (UQ), highlighting the necessity for a comprehensive algorithmic framework. This presentation proposes a novel algorithm framework for the optimization and UQ of MEMS devices. This approach is applied to several MEMS devices to verify its effectiveness.

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Speech Title: High-precision micro inertial sensors based on the 2D PhC optomechanic cavity

Abstract: Cavity optomechanics is a kind of structure that can exhibit both optical resonances and mechanical oscillations, and such phenomena can interact strongly to each other. It is an emerging platform for fundamental physics and engineering application investigations. Recent years, several kinds of micro/nano structures can be used to realize cavity optomechanics. Among of those cavity optomechanical structures, the 1D/2D nano photonic crystal (PhC) beam/slab can be widely used to achieve highly-integrated sensors, because of that it can be co-fabricated with conventional MEMS and CMOS procedures. In this talk, we would like to summarize our recently research achievements on the demonstrations of cavity optomechanical micro inertial sensors based on the planar 2D PhC slabs, including the single-axis accelerometers and gyroscopes. We will introduce the operating principles, design guides, fabrication procedures, and measurements results for the proposed accelerometer and gyroscope. And in the end, we will also discuss the challenges for achieving the really integrated cavity optomechanical inertial sensors, for examples, the integrated laser sources, polarization controllers, high-efficient waveguides and couplers, and the photodetectors and vacuum packages.

Chenguo Hu is a professor of physics in Chongqing University. Associate Editor of Nano Energy and Research. She earned her Ph.D. from Chongqing University in 2003. Her research focuses on surface and interface physics, and related functional devices, particularly making innovative contributions to the fields of triboelectric nanogenerators and self-powered sensors. She has published more than 350 papers in Sci. Adv., Nature Commun., Sci. Robotics, Nature Sustain., Adv. Mater., EES, etc. with more than 28,000 SCI citations. Her h-index is 88. She has won 2 first and 2 second prizes in provincial-level Natural Science Awards. She is an expert who received the state council issued a Special Government Allowances. She was recognized as a Highly Cited Researcher by Clarivate Analytics in 2022-2025.

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Abstract: Triboelectric nanogenerator (TENG) is a new technology for collecting low-frequency mechanical energy, which is one of the most important technologies to solve distributed energy supply of wireless sensor networks where the wired electricity is not available. However, it is a great challenge to improve the output energy density, reduce interface abrasion and efficiently extract energy for electronics. Herein, I will introduce the strategies adopted in our research group to improve the output performance and durability, and energy utilization of the TENG, which include (1) boosting output charge density enabled by charge excitation strategy, self-polarization of polar high-k materials, charge trapping failure of dielectric polymers, ultra-fast charge injection technique, charge migration in dielectric materials, etc., (2) improving durability, and (3) efficient energy managements. Through multi-dimensional analysis and regulation of charge behavior, these works have made substantial advancements in optimizing the performance of TENG and provided a theoretical foundation for the development of high-energy-density, self-powered TENG systems.

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Abstract: In view of sustainable energy sources to power the Intemet of things, recently a new kind of ambientenergy harvesting mechanism has been developed. It is named nanogenerator to efficiently harvest andconvert biomechanical energy into remarkable electricity, which is thus clean without any source ofpollution and sustainable. However, in order for these nanogenerators to make a real impact in dailylife applications, further improvements are needed in particular for materials innovation. This talk willintroduce our research activities to respond these challenges by developing an emerging and promisingmaterial called silk fibroin. More than effective energy harvesting, we also focus on inventing self-powered smart electronics, which combine separated functional components, including a completechain of sensing-proceeding-responding as well as power sources

Bo Zhao received the Ph.D. degree from EE Department of Tsinghua University, Beijing, China, in 2011. He worked in National University of Singapore and UC Berkeley (BWRC) from 2013 to 2018. Since 2018, he has been a Professor with the Institute of VLSI Design, Zhejiang University, Hangzhou, China. In 2024, he was rated as Qiushi Distinguished Professor of Zhejiang University. He has authored or coauthored more than 70 articles and book chapters. He holds more than 30 Chinese patents. His research interests include IoT radios, power harvesters, biomedical sensors, wireless systems, and wearable/implantable radios. Dr Zhao was a recipient of the 2017 IEEE Transactions of Circuits and Systems Darlington Best Paper Award, the Design Contest Award of the 2013 IEEE International Symposium on Low Power Electronics and Design, the Best Associate Editor of IEEE Transactions on Circuits and Systems I, as well as the Best Paper Award of the 2024 IEEE International Conference on Integrated Circuits Technologies and Applications (ICTA). He works as the International Technical Program Committee (ITPC) member of ISSCC. He was the Publication Chair for the 2016 IEEE Biomedical Circuits and Systems Conference. He serves as the Chair of IEEE Biomedical and Life Science Circuits and Systems Society, as well as an Associate Editor for the IEEE Transactions on Biomedical Circuits and Systems (2024-now)，IEEE Transactions on Circuits and Systems I (2020-2023), and IEEE Transactions on Circuits and Systems II (2026-now). Dr. Zhao also serves as a Committee Member of IEEE/C/SM.

Speech Title: Wireless Biomedical Sensors--From ‘Near Zero Power’ to ‘Battery Free’

Abstract: Wireless sensors are widely used in biomedical applications, such as ExG (EEG, EMG, and ECG) monitoring, neural recording, continuous glucose monitoring (CGM), cerebral oxygen monitoring (COM), touch sensing, temperature sensors, etc. A low-power wireless-sensing chip is required to enable a miniature battery size and a long lifetime. However, the battery in a traditional wireless sensor still adds to the overall size and needs periodic replacement. In comparison, battery-free wireless techniques lower the body burden and mitigate the biological responses for wearables and implants, respectively. In addition, the battery-free wireless techniques offer infinite life time for the biomedical sensors. In this talk, the wireless biomedical sensors will be listed and described, from near-zero-power techniques to battery-free techniques, including our recent works published in ISSCC and JSSC.

Dr. Shih-Chi Chen is a Professor in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. He received his B.S. degree in Mechanical Engineering from the National Tsing Hua University, Taiwan, in 1999; and his S.M. and Ph.D. degrees in Mechanical Engineering from the Massachusetts Institute of Technology, U.S., in 2003 and 2007, respectively. Following his graduate work, he entered a post-doctoral fellowship in the Wellman Center for Photomedicine, Harvard Medical School, where his research focused on biomedical optics and endomicroscopy. From 2009 to 2011, he was a Senior Scientist at Nano Terra, Inc., a start-up company founded by Prof. George Whitesides at Harvard University, to develop precision instruments for novel nanofabrication processes. His current research interests include ultrafast laser applications, biomedical optics, precision engineering, and nanomanufacturing. Dr. Chen is a Fellow of Optica (formerly OSA), Fellow of SPIE, Fellow of American Society of Mechanical Engineers (ASME), and members of the American Society for Precision Engineering (ASPE) and Hong Kong Young Academy of Sciences (YASHK). He currently serves as the Associate Editor of ASME Journal of Micro- and Nano-Manufacturing, IEEE Transactions on Nanotechnology, and HKIE Transactions. In 2003 and 2018, he received the prestigious R&D 100 Awards for developing a six-axis nanopositioner and an ultrafast nanoscale 3-D printer respectively.

Speech Title: Closed-loop Two-photon Lithography based on a Single-cavity Dual- comb Laser

Abstract: Two-photon lithography (TPL) has been widely used to fabricate various 3D structures and devices with better than 200-nm resolution. Yet, a major challenge that has been consistently overlooked is its limited reproducibility, where nanoscale features can only be realized via careful process tuning, making it impractical for industrial applications. Although metrology methods such as atomic force microscopy provide precise surface morphology, such post fabrication measurement cannot address the low reproducibility issue. Here, we present a TPL platform based on a single-cavity dual-comb laser, where reproducibility issue. Here, we present a TPL platform based on a single-cavity dual-comb laser, where 90% of the first comb’s power performs continuous TPL at 800 nm (after wavelength conversion) and simultaneously the dual-comb system performs in-situ phase measurement at 1052 nm over the entire work field, generating instantaneous phase and height information at ~360 Hz. The measured phase information is fed to a dynamic model for modulating the printing parameters (including laser power, scanning rate, and exposure time) to correct height errors in a fully automated fashion. In the experiments, we demonstrated continuous fabrication of millimeter-scale diffractive optical elements experiments, we demonstrated continuous fabrication of millimeter-scale diffractive optical elements and Fresnel zone plate lenses over 14 hours with substantially improved signal-to-noise ratios, process repeatability, and focus quality. These results show that the closed-loop dual-comb platform is a cost-effective solution for high-precision TPL.

Dr. Peng Xi is a Boya distinguished professor at College of Future Technology, Peking University, China. His research interest is on the development of optical super-resolution microscopy techniques. He is a Fellow of OPTICA and IAAM. He has been awarded the National Distinguished Young Scholar. He is the Chief scientist of the Key Research and Development Plan of the Ministry of Science and Technology. He is on the editorial board of 5 SCI-indexed journals such as Light and Advanced Photonics. He has published over 120 scientific journal papers on peer-reviewed journals including Nature, Nature Photonics, Nature Methods, etc., with more than 9,000 citations. He has been granted 3 American patents and over 20 Chinese patents, and delivered many plenary talks on international conferences hosted by SPIE and OPTICA.

Speech Title: Super-resolution structured illumination microscopy: polarization, anisotropy, and lattice

Abstract: This report presents two recent research advances from our work: I) Utilizing polarization-structured illumination microscopy (Polar-SIM) technology, we can extract information on the molecular arrangement within organelles, such as actin-myosin dynamic interactions and the spatial organization between actin and spectrin in neuronal axons. Furthermore, we developed fluorescence anisotropy SIM (FA-SIM), which significantly enhances the spatiotemporal resolution of fluorescence anisotropy (FA) measurements. Live-cell dynamic imaging with FA-SIM has revealed dynamic changes in macromolecular crowding levels within organelles at super-resolution. II) Conventional 2D-SIM employs one-dimensional stripe patterns and rotates them at three angles to achieve uniform resolution. Here, to alleviate photobleaching and improve the temporal resolution of 2D-SIM, we develop triangle-beam interference SIM (3I-SIM), which generates a two-dimensional lattice pattern based on 3 radially polarized beam interference, which enhances the signal-to-noise ratio of the high-frequency components. Compared to conventional 2D-SIM, 3I-SIM reduces photobleaching and improves the temporal resolution to 242 Hz. Benefiting from the unidirectional phase shift, 3I-SIM provides 3-fold higher rolling frame rate than 2D-SIM to visualize fast biological dynamics.

Guojun Zhang received the Ph.D. degree from Northwestern Polytechnical University, China, in 2015. He is currently a Professor with the School of Instrument and Electronics, North University of China, and serves as the Deputy Director of the State Key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument at North University of China. His current research interests include MEMS technology, acoustic transducers, and sensing systems.

Speech Title: Biomimetic MEMS Vector Hydrophones and Applications

Abstract: MEMS biomimetic vector hydrophones are inspired by the lateral line system and hair-cell structures of aquatic organisms and have attracted significant attention for underwater acoustic sensing due to their small size, high sensitivity, directional capability, and ease of integration. This work reviews the development and application of MEMS-based micro/nano hydrophones, focusing on three representative types: biomimetic vector hydrophones, optical microcavity hydrophones, and piezoelectric thin-film hydrophones. Among them, the biomimetic vector hydrophone mimics biological hair-cell sensing mechanisms to detect acoustic pressure or particle velocity. Its key research aspects include sensing mechanisms, biomimetic structural design, micro/nano fabrication technologies, packaging and calibration, and typical application scenarios. Optical microcavity hydrophones achieve high sensitivity through resonant cavity structures, while piezoelectric thin-film hydrophones convert acoustic signals into electrical responses using piezoelectric materials, providing alternative approaches for miniaturized underwater acoustic sensors. In practical applications, MEMS biomimetic vector hydrophones can be integrated with turbulence sensors and buoy-based microsystems to form compact ocean monitoring platforms. Such systems typically include acoustic modules, navigation components, surface and underwater electronic units, and signal processing and direction-finding algorithms. Through integrated fabrication, environmental testing, and performance evaluation, these systems enable underwater acoustic detection and turbulence measurements. The results demonstrate that MEMS biomimetic vector hydrophones offer promising potential for compact, low-power, and high-performance underwater sensing systems.

Dr. Kebin Shi received his Bachelor's and Master’s degree from Nankai University in 1998 and 2001 respectively. He received his Ph.D degree in Electrical Engineering at the Pennsylvania State University in 2007. Dr. Shi joined faculty members at Peking University in the Institute of Modern Optics in July 2011. His research focuses on developing high resolutioin photonic systems and devices based on ultrafast/nonlinear optical principles for spectroscopy, imaging and applications. His recent research interests include super-resolution imaging, nonlinear holography and femto-second frequency comb metrology. He currently serves as a co-chair of conference committee for “Ultrafast Imaging and Spectroscopy Conference” at SPIE Optics + Photonics meeting. In 2013, Dr. Shi was awarded “National Natural Science Funds for Excellent Young Scholar” by National Natural Science Foundation of China (NNSFC). He has authored or coauthored more than 100 refereed journal papers with over 3000 citations (h-index: 30), and has delivered over 50 invited talks/seminars in international or domestic conferences/universities. His scientific achievements also include ~20 granted patents. He currently serves as deputy editor of Optics Express.

Speech Title: High Resolution Optical Imaging Enabled by Field Engineering

Abstract: Optical imaging has been playing important roles in modern physics and engineering. The unique capability of high resolution in both temporal and spatial dimensions make optical imaging technologies essential elements in various interdisciplinary researches. Many outstanding imaging technologies are developed based on the fusion of basic optical-physics mechanisms and engineering implementations. In this talk, the introduction to fundamental concepts enabling highly spatiotemporal resolution will be discussed, followed by the examples in engineering applications including novel label free imaging and tomographic metrology.

Dr. Chao Zuo is a Zijin Chair Professor at Nanjing University of Science and Technology (NJUST), Distinguished Professor of "Changjiang Scholars Program", Ministry of Education of China. He leads the Smart Computational Imaging Laboratory (SCILab: www.scilaboratory.com) at the School of Electronic and Optical Engineering, NJUST, and is also the founder and director of the Smart Computational Imaging Research Institute of NJUST. He has long been engaged in the development of novel Computational Optical Imaging and Measurement technologies, with a focus on Phase Measuring Imaging Metrology. He has published > 300 peer-reviewed articles with over 18,000 citations. These researches have been featured on journal cover (including Light, Optica, LPR, PhotoniX, AP, etc.) over 40 times. He currently serves as an Associate Editor of eLight, PhotoniX, OL, OLEN, and IEEE TCI. He is a Fellow of SPIE | Optica | IOP, and listed as a Clarivate Highly Cited Researcher.

Speech Title: High-speed structured light 3D imaging empowered by deep learning

Abstract: With the rapid development of optoelectronic information technology, three-dimensional (3D) imaging and sensing has become a research forefront in optical metrology. Fringe projection profilometry (FPP) is one of the most representative 3D imaging technologies due to its non-contact, high-resolution, high-speed, and full-field measurement capability. In recent years, with the rapid advances of optoelectronic devices and digital signal processing units, people subsequently set higher expectations on FPP: it should be both “high precision” and “high speed”. While these two aspects seem contradictory in nature, “speed” has gradually become a fundamental factor that must be taken into account when using FPP, and high-precision 3D reconstruction using only one single pattern has been the ultimate goal of structured light 3D imaging in perpetual pursuit. Nowadays, deep learning technology has fully “permeated” into almost all tasks of optical metrology. In this talk, we introduce our recent efforts to apply deep-learning approaches to FPP. We show that the deep-learning-enabled fringe analysis approach can significantly boost the accuracy and improve the quality of the phase reconstruction compared to conventional single-fringe phase retrieval approaches. Deep learning can also be used to achieve single-frame, high-precision, unambiguous 3D shape reconstruction, which is expected to fill the speed “gap” between 3D imaging and 2D sensing and enables FPP techniques to go a step further in high-speed and high-accuracy 3D surface imaging of transient events.

Qiang Ren is the Deputy General Manager of Zhejiang Xindong Technology Co., Ltd. With more than 20 years of experience in MEMS process development, he has successfully developed MEMS inertial products (3-axis accelerometer + gyroscope) from scratch to mass production and large-scale on-vehicle application, developed the mass production process of brain-computer interface chips, and developed pressure chips that have been mass-produced and applied in vehicles. He is proficient in the foundry processes of various MEMS chips, and strives for perfection in the foundry products for customers to create sustainable value

Speech Title: Characteristic Processes for MEMS Fabrication, Packaging and Testing

Abstract: Zhejiang Xindong Technology Co., Ltd. is located in Jiaxing City, Zhejiang Province, specializing in high-end MEMS foundry services with independent intellectual property rights. The company is equipped with an advanced 6-inch MEMS chip industrial production line, and its process capabilities cover the entire manufacturing cycle of MEMS chips. It has 10-level and 100-level clean rooms. Through years of dedicated research and development in foundry technology, it has established a full range of standardized process platforms, including inertial sensor platforms, gas sensor platforms, pressure sensor chip platforms, and microfluidic chip platforms. We have successfully achieved mass production of various products such as brain-computer interface chips, accelerometers, gyroscopes, pressure sensors, microfluidic devices, and gas sensors, forming a complete system integrating MEMS process design and standardized mass production. We provide one-stop technical services including micro-nano device research and development, process foundry, and performance testing for domestic and foreign fabless enterprises. Since its establishment, the company has completed the research and verification of over 300 types of sensitive chips, providing professional chip manufacturing technical support to more than 100 domestic research institutions, universities, and private enterprises. At the same time, it has made significant contributions to the rapid domestic substitution of core components for many enterprises.

Dr. Guoliang Huang is currently a Chair Professor of college of engineering at Peking University, and Changjiang Chair Professor at Peking University. He received his Ph.D. degree from University of Alberta, Canada in 2004. Dr. Huang’s research interests include wave propagation and mechanics in elastic/acoustic metamaterials and structural materials, active mechanics, topological wave mechanics, structural dynamics, vibration and sound wave suppression. He has authored one book, 7 book chapters and more than 200 journal papers (include Nature Reviews Materials, Nature Communications, Proceedings of the National Academy of Sciences (PNAS), Advanced Materials, Physical Review Letters, Journal of Mechanics and Physics of Solids, et al.), with H-index of 71. He is the Associate Editor of Wave Motion, Associate Editor of the ASME Journal of Applied Mechanics, the ASME Journal of Vibration and Acoustics, the field Editor-in-Chief of Frontiers in Physics, and serves in editorial broad in many other journals.

Speech Title: A controllable anti-P-pseudo-Hermitian mechanical system and its application

Abstract: A novel anti-P-pseudo-Hermitian mechanical system that integrates piezoelectric actuators and sensors with non-reciprocal coupling into mechanical beams is proposed. This configuration enables the system to exhibit programmable exceptional points (EPs), which are critical for enhancing sensitivity in sensing applications. Our theoretical analysis, supported by numerical simulations and experimental validation, demonstrates the system's capability to detect minute mass variations and identify surface cracks with high precision. This advancement not only contributes to the field of non-Hermitian physics but also paves the way for the development of next-generation mechanical sensors leveraging EP physics.

Dean and Professor/Doctoral Supervisor of the School of Power and Mechanical Engineering at Wuhan University, as well as a professor at the School of Integrated Circuits, School of Electrical Engineering and Automation, and Zhongnan Hospital. He is a core member of Professor Liu Sheng's team. He is a recipient of the National Natural Science Foundation of China Outstanding Youth Fund (Project A) and a national-level youth talent program awardee. He earned his Ph.D. and conducted postdoctoral research at the University of Cambridge. He completed his undergraduate studies at the School of Physics, Peking University, and obtained his master's degree from the School of Materials Science and Engineering at the Georgia Institute of Technology. He received his Ph.D. from the Department of Engineering at the University of Cambridge, under the supervision of Professor John Robertson, a Fellow of the Royal Society and the Royal Academy of Engineering. He has previously worked as a researcher at the University of Cambridge and Harvard University. His research focuses on theoretical calculations and experimental studies in areas such as micro-nano fabrication, semiconductor devices and device co-design, wide bandgap semiconductors, ceramic packaging, and high-entropy alloys. He has published over 200 SCI-indexed papers, including communications as the corresponding author in leading international journals such as Nature, Nature Materials, Nature Communications, and Science Advances, with over 10,000 citations. He has led multiple key projects funded by the Organization Department of the Central Committee of the CPC, the Ministry of Science and Technology, the National Natural Science Foundation of China, China Southern Power Grid, and the State Grid Corporation of China.

Speech Title: Systematic investigation of temperature dependences of elastic constants of heavily doped silicon via machine learning-based molecular dynamics

Abstract: Accurate knowledge of the elastic behavior of doped silicon across a wide temperature range is critical for the design of temperature-stable microelectromechanical systems (MEMS) resonators. In this work, we present a machine learning-assisted molecular dynamics framework to systematically investigate the temperature-dependent elastic constants of heavily phosphorus-doped silicon. A deep potential (DP) model is developed within the Deep Potential Generator (DP-GEN) framework using first-principles data from density functional theory (DFT) calculations. The model captures atomic interactions in doped silicon systems with near-DFT accuracy while enabling large-scale molecular dynamics simulations. Using this approach, we compute the elastic constants of silicon doped at concentrations of 5 × 1019 cm⁻³, 1 × 1020 cm-3, and 1.5 × 1020 cm-3 over a temperature range relevant to MEMS operation. To validate the model, we fabricate and characterize MEMS resonators with multiple vibrational modes using ultra-heavily doped silicon wafers, extracting the temperature coefficients of elastic constants experimentally. The simulated results show excellent agreement with experimental measurements, with relative errors below 5%, confirming the predictive capability and transferability of the DP model. This work establishes a reliable and scalable computational framework for characterizing the thermoelastic behavior of doped silicon and provides valuable insights for the design of frequency-stable MEMS devices.

Dr. Wei Wang is Peking University Boya Distinguished Professor and director of National Key Laboratory of Advanced Micro and Nano Manufacture Technology, China. He has long been dedicated to research in MEMS yield, MEMS integration, polymer micro/nano-fabrication, clinical micro/nano-systems, and thermal management for 3D-stacked ICs. He has authored over 190 academic papers in prestigious journals and conferences such as Lab on a Chip , IEEE MEMS , and ECTC . Furthermore, he holds more than 40 granted invention patents, including two U.S. patents. He is the associate editor-in-chief of Microfluidics and Nanofluidics (Nature), and editor of Microsystems & Nanoengineering (Nature).

Speech Title: AI-Driven MEMS Intelligent Manufacturing

Abstract: As Micro-Electro-Mechanical Systems (MEMS) advance toward higher integration and complex multi-physics coupling, traditional manufacturing paradigms based on empirical "trial-and-error" face significant limitations in precision and development speed. Artificial intelligence (AI) has been proven to be a transformative engine that can empowers a broad spectrum of intelligent manufacturing paradigms. Building upon this foundation, this report proposes an AI-driven intelligent manufacturing framework for MEMS and demonstrates how these AI techniques effectively mitigate systemic uncertainty in the fabrication lifecycle. For critical single-step processes characterized by high uncertainty—such as Deep Reactive Ion Etching (DRIE)—the AI model achieves high-fidelity predictions of process outputs by mapping equipment parameters to physical outcomes, significantly reducing experimental costs and enabling automated searches for the optimal process recipe. In complex process chains where multiple stages are superimposed, the AI algorithms process high-dimensional data from Manufacturing Execution Systems (MES) to identify hidden correlations between process deviations and final device performance, thereby pinpointing the "killer variables" responsible for yield loss. The integration of AI in MEMS fabrication signifies a transformative shift from reactive trial-and-error toward intelligent manufacturing. This paradigm not only accelerates process development cycles and enhances manufacturing yields but also establishes the methodological foundation for transitioning MEMS fabs into digital twin manufacturing ecosystems.

Dr. Chengkuo Lee is Optica Fellow and director of the Center for Intelligent Sensors and MEMS at the National University of Singapore, Singapore. His Google Scholar citation is 50,000+. He is also recognized as one of the 21 most influential professors at NUS. Furthermore, he is Highly Cited Researcher Designation in 2023 and 2024 (Clarivate). He is the associate editor-in-chief of Trans. Nanotechnology (IEEE), editor-in-chief of AI Sensors (MDPI), and editor-in-chief of npj Self-powered Electronics (Nature). He is the Executive Editor of J Micromechanics and Microeng. (IOP, UK). He is the Associate Editor of J. MEMS (IEEE), Chip (Elsevier), and Internet of Things (Elsevier).

Speech Title: AI Sensors and Edge AI Applications

Abstract: With the growing demand for energy-efficient AI applications, the rapid development of self-powered sensors together with edge computing and edge AI technology at the sensor nodes has led to the new era of AI sensors. Traditional sensors and sensing systems can no longer meet the demands for real-time multimodal sensing and large-scale data processing, leading to a shift towards a new paradigm of AI Sensors and Artificial Intelligence of Things (AIoT) sensing systems with integrated computational intelligence. Self-powered wearable sensors have promoted low-power or battery-free sensing platforms for applications including human-machine interaction, soft robotics, and electronic skin (e-Skin). By embedding triboelectric, piezoelectric, electromagnetic and hybrid nanogenerators directly into the sensing structure, the same transducer now harvests ambient mechanical, vibrational or thermal energy while generating electrical signals based on stimuli for sensing, creating genuinely self-sustained sensor nodes. Breathable tribo-ferroelectric textiles, hydrogel-based nano-generators and silk-derived e-skins exemplify this dual-function strategy, delivering gesture-tracking, pressure–temperature mapping and long-term physiological monitoring without external batteries. Furthermore, the emergence of "in-sensor computing and near-sensor computing" demonstrates the sensor nodes or edge applications with computational capabilities, reducing data transmission burdens while enhancing privacy and energy efficiency. Using optical edge computing platform advanced by Si Photonics sensors, optical edge AI sensors have become an attractive approach for enabling intelligent, energy-efficient, adaptive sensing networks.

Professor Aaron Ho-Pui HO received his BEng and PhD in Electrical and Electronic Engineering from the University of Nottingham. Currently a professor at the Department of Biomedical Engineering, The Chinese University of Hong Kong (CUHK), his previous appointments include Associate Professor at the Department of Electronic Engineering, Associate Dean of Engineering, CUHK; Assistant Professor in Department of Physics and Materials Science, City University of Hong Kong; Senior Process Engineer for semiconductor laser fabrication in Hewlett-Packard. His service in the professional and academic community includes Chairman of Hong Kong Optical Engineering Society; Chairman of IEEE Electron Device/Solid-State Circuits (ED/SSC) Hong Kong Chapter, Admission Panel member of Technology Business Incubation Programme (IncuTech) operated by Hong Kong Science and Technology Parks Corporation (HKSTP); Council Member of The Technological and Higher Education Institute of Hong Kong (THEi). His current academic interests focus on nano-sized semiconductor materials for photonic and sensor applications, optical instrumentation, surface plasmon resonance biosensors, lab-on-a-chip, solid-state nanopore and biophotonics. He has published over 400 peer-reviewed articles, 33 Chinese and 6 US patents. He is a Fellow of SPIE.

Speech Title: Single Molecule Analysis with a Pyramidal Silicon Nanopore

Abstract: We report herein a new solid-state nanopore device platform with which one can readily conduct rapid analysis of single molecules. The long-standing challenge of this topic is that currently such nanopores are based on the use of membrane proteins, which has a nanopore aperture of approximately 1 nm, is the sole option for practical nanopore devices. However, biological nanopores are extremely delicate, and they have limited chemical resistance and short operational lifetime. To address these issues, we have developed a new solid-state device platform featuring a pyramidal sub-nm oxidized nanopore (PSON). The device is fabricated via batch-mode processing relying solely on wet etching, thus enabling a drastic reduction in manufacturing costs. By utilizing the ultrathin oxide-coated silicon nanopore as an additional electrode, PSON further enables a 3-terminal sensing configuration. Experimental results demonstrate exceptionally promising performance for singlemolecule characterization and sequencing. The 3-terminal configuration provides a two-fold improvement in signal-to-noise ratio relative to conventional ionic current blockade measurements. We have successfully sequenced ssDNA and peptide samples of various lengths in a single-read operation. Leveraging PSON’s high chemical resistance, we have achieved real-time monitoring and sequencing of reaction products, specifically, the dissociation of a protein-DNA complex (via in situ pH modulation) and the enzymatic digestion of insulin to release its two constituent peptide chains. Furthermore, our PSON prototype exhibits no detectable performance degradation following a cumulative sequencing duration exceeding 150 hours.