Issue

Vol. 17 (2025)

Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides

https://doi.org/10.1007/s40820-025-01743-y
Zhihao Ren
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Zixuan Zhang
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Yangyang Zhuge
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Zian Xiao
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Siyu Xu
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Jingkai Zhou
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Chengkuo Lee
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore

Corresponding Author: Chengkuo Lee

elelc@nus.edu.sg

Nano-Micro Letters, Vol. 17 (2025), Article Number: 261

Abstract

The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (< 10 ns), and minimal energy consumption (< 0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces, marking a pivotal advancement in edge computing and AI deployment.


Highlights:
1 A novel near-sensor edge computing system integrates aluminum nitride (AlN) microrings for photonic feature extraction and Si Mach–Zehnder interferometers for photonic neural network operations, achieving real-time artificial intelligence (AI) processing.
2 Demonstrates high classification accuracy (96.77% for gestures, 98.31% for gaits) with low latency (< 10 ns) and minimal energy consumption (< 0.34 pJ).
3 Enables low-power, high-speed AI applications with seamless hybrid photonic-electronic integration on a bilayer AlN/Si waveguide platform.

Keywords

Photonic integrated circuits Edge computing Aluminum nitride Neural networks Wearable sensors
Ren, Zhihao, Zixuan Zhang, Yangyang Zhuge, Zian Xiao, Siyu Xu, Jingkai Zhou, and Chengkuo Lee. 2025. “Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides”. Nano-Micro Letters 17 (May):261. https://doi.org/10.1007/s40820-025-01743-y.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. F.K. Shaikh, S. Karim, S. Zeadally, J. Nebhen, Recent trends in Internet-of-things-enabled sensor technologies for smart agriculture. IEEE Internet Things J. 9(23), 23583–23598 (2022). https://doi.org/10.1109/JIOT.2022.3210154
  2. A. Mehonic, A.J. Kenyon, Brain-inspired computing needs a master plan. Nature 604(7905), 255–260 (2022). https://doi.org/10.1038/s41586-021-04362-w
  3. L.D. Stein, B.M. Knoppers, P. Campbell, G. Getz, J.O. Korbel, Data analysis: create a cloud commons. Nature 523(7559), 149–151 (2015). https://doi.org/10.1038/523149a
  4. J.-H. Kang, H. Shin, K.S. Kim, M.-K. Song, D. Lee et al., Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions. Nat. Mater. 22(12), 1470–1477 (2023). https://doi.org/10.1038/s41563-023-01704-z
  5. A.V. Babu, T. Zhou, S. Kandel, T. Bicer, Z. Liu et al., Deep learning at the edge enables real-time streaming ptychographic imaging. Nat. Commun. 14(1), 7059 (2023). https://doi.org/10.1038/s41467-023-41496-z
  6. B. Li, P. Chen, H. Liu, W. Guo, X. Cao et al., Random sketch learning for deep neural networks in edge computing. Nat. Comput. Sci. 1(3), 221–228 (2021). https://doi.org/10.1038/s43588-021-00039-6
  7. C. Liu, H. Chen, S. Wang, Q. Liu, Y.G. Jiang et al., Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15(7), 545–557 (2020). https://doi.org/10.1038/s41565-020-0724-3
  8. S.G. Kim, J.S. Han, H. Kim, S.Y. Kim, H.W. Jang, Recent advances in memristive materials for artificial synapses. Adv. Mater. Technol. 3(12), 1800457 (2018). https://doi.org/10.1002/admt.201800457
  9. H. Veluri, U. Chand, C.-K. Chen, A.V. Thean, A low-latency DNN accelerator enabled by DFT-based convolution execution within crossbar arrays. IEEE Trans. Neural Netw. Learn. Syst. 36(1), 1015–1028 (2025). https://doi.org/10.1109/TNNLS.2023.3327122
  10. C. Wang, X. Xu, X. Pi, M.D. Butala, W. Huang et al., Neuromorphic device based on silicon nanosheets. Nat. Commun. 13, 5216 (2022). https://doi.org/10.1038/s41467-022-32884-y
  11. X. Feng, S. Li, S.L. Wong, S. Tong, L. Chen et al., Self-selective multi-terminal memtransistor crossbar array for in-memory computing. ACS Nano 15(1), 1764–1774 (2021). https://doi.org/10.1021/acsnano.0c09441
  12. X. Yan, J.H. Qian, V.K. Sangwan, M.C. Hersam, Progress and challenges for memtransistors in neuromorphic circuits and systems. Adv. Mater. 34(48), e2108025 (2022). https://doi.org/10.1002/adma.202108025
  13. X. Guo, W. Yang, X. Zou, A sensor system integrating sensing and intelligence based on MEMS reservoir computing. J. Phys. Conf. Ser. 2740(1), 012013 (2024). https://doi.org/10.1088/1742-6596/2740/1/012013
  14. X. Guo, W. Yang, Y. Bai, X. Xiong, Z. Wang et al., Optimizing temporal data forecasting for stiffness-modulated MEMS reservoir computing. IEEE Sens. J. 24(22), 38092–38101 (2024). https://doi.org/10.1109/JSEN.2024.3446672
  15. H. Nikfarjam, M. Megdadi, M. Okour, S. Pourkamali, F. Alsaleem, Energy efficient integrated MEMS neural network for simultaneous sensing and computing. Commun. Eng. 2, 19 (2023). https://doi.org/10.1038/s44172-023-00071-6
  16. X. Guo, W. Yang, X. Xiong, Z. Wang, X. Zou, MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge. Microsyst. Nanoeng. 10, 84 (2024). https://doi.org/10.1038/s41378-024-00701-9
  17. T. Wan, B. Shao, S. Ma, Y. Zhou, Q. Li et al., In-sensor computing: materials, devices, and integration technologies. Adv. Mater. 35(37), 2203830 (2023). https://doi.org/10.1002/adma.202203830
  18. F. Zhou, Y. Chai, Near-sensor and in-sensor computing. Nat. Electron. 3(11), 664–671 (2020). https://doi.org/10.1038/s41928-020-00501-9
  19. Z. Zhang, X. Zhao, X. Zhang, X. Hou, X. Ma et al., In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array. Nat. Commun. 13(1), 6590 (2022). https://doi.org/10.1038/s41467-022-34230-8
  20. D. Lee, M. Park, Y. Baek, B. Bae, J. Heo et al., In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing. Nat. Commun. 13(1), 5223 (2022). https://doi.org/10.1038/s41467-022-32790-3
  21. B. Bae, M. Park, D. Lee, I. Sim, K. Lee, Hetero-integrated InGaAs photodiode and oxide memristor-based artificial optical nerve for in-sensor NIR image processing. Adv. Opt. Mater. 11(3), 2201905 (2023). https://doi.org/10.1002/adom.202201905
  22. L. Pi, P. Wang, S.-J. Liang, P. Luo, H. Wang et al., Broadband convolutional processing using band-alignment-tunable heterostructures. Nat. Electron. 5(4), 248–254 (2022). https://doi.org/10.1038/s41928-022-00747-5
  23. Y. Chen, M. Nazhamaiti, H. Xu, Y. Meng, T. Zhou et al., All-analog photoelectronic chip for high-speed vision tasks. Nature 623(7985), 48–57 (2023). https://doi.org/10.1038/s41586-023-06558-8
  24. F. Zhou, Z. Zhou, J. Chen, T.H. Choy, J. Wang et al., Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14(8), 776–782 (2019). https://doi.org/10.1038/s41565-019-0501-3
  25. Y. Zhou, J. Fu, Z. Chen, F. Zhuge, Y. Wang et al., Computational event-driven vision sensors for in-sensor spiking neural networks. Nat. Electron. 6(11), 870–878 (2023). https://doi.org/10.1038/s41928-023-01055-2
  26. J.-K. Han, I.-W. Tcho, S.-B. Jeon, J.-M. Yu, W.-G. Kim et al., Self-powered artificial mechanoreceptor based on triboelectrification for a neuromorphic tactile system. Adv. Sci. 9(9), e2105076 (2022). https://doi.org/10.1002/advs.202105076
  27. F. Liao, Z. Zhou, B.J. Kim, J. Chen, J. Wang et al., Bioinspired in-sensor visual adaptation for accurate perception. Nat. Electron. 5(2), 84–91 (2022). https://doi.org/10.1038/s41928-022-00713-1
  28. S. Lee, R. Peng, C. Wu, M. Li, Programmable black phosphorus image sensor for broadband optoelectronic edge computing. Nat. Commun. 13(1), 1485 (2022). https://doi.org/10.1038/s41467-022-29171-1
  29. H. Jang, H. Hinton, W.-B. Jung, M.-H. Lee, C. Kim et al., In-sensor optoelectronic computing using electrostatically doped silicon. Nat. Electron. 5(8), 519–525 (2022). https://doi.org/10.1038/s41928-022-00819-6
  30. L. Mennel, J. Symonowicz, S. Wachter, D.K. Polyushkin, A.J. Molina-Mendoza et al., Ultrafast machine vision with 2D material neural network image sensors. Nature 579(7797), 62–66 (2020). https://doi.org/10.1038/s41586-020-2038-x
  31. J. Meng, T. Wang, H. Zhu, L. Ji, W. Bao et al., Integrated in-sensor computing optoelectronic device for environment-adaptable artificial retina perception application. Nano Lett. 22(1), 81–89 (2022). https://doi.org/10.1021/acs.nanolett.1c03240
  32. T. Wang, M.M. Sohoni, L.G. Wright, M.M. Stein, S.-Y. Ma et al., Image sensing with multilayer nonlinear optical neural networks. Nat. Photon. 17(5), 408–415 (2023). https://doi.org/10.1038/s41566-023-01170-8
  33. D. Li, H. Zhou, Z. Ren, C. Lee, Advances in MEMS, optical MEMS and nanophotonics technologies for volatile organic compound detection and applications. Small Sci. 5(4), 2400250 (2025). https://doi.org/10.1002/smsc.202400250
  34. S.-W. Lee, M. Kang, J.-K. Han, S.-Y. Yun, I. Park et al., An artificial olfactory sensory neuron for selective gas detection with in-sensor computing. Device 1(3), 100063 (2023). https://doi.org/10.1016/j.device.2023.100063
  35. V.M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer et al., Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro. Surveill. 25(3), 2000045 (2020). https://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045
  36. Y. Wang, Y. Gong, L. Yang, Z. Xiong, Z. Lv et al., MXene-ZnO memristor for multimodal in-sensor computing. Adv. Funct. Mater. 31(21), 2100144 (2021). https://doi.org/10.1002/adfm.202100144
  37. C. Wang, H. Niu, G. Shen, Y. Li, Self-healing hydrogel-based triboelectric nanogenerator in smart glove system for integrated drone safety protection and motion control. Adv. Funct. Mater. (2024). https://doi.org/10.1002/adfm.202419809
  38. Y. Li, Z. Qiu, H. Kan, Y. Yang, J. Liu et al., A human-computer interaction strategy for an FPGA platform boosted integrated “perception-memory” system based on electronic tattoos and memristors. Adv. Sci. 11(39), 2402582 (2024). https://doi.org/10.1002/advs.202402582
  39. H. Zhang, H. Li, Y. Li, Biomimetic electronic skin for robots aiming at superior dynamic-static perception and material cognition based on triboelectric-piezoresistive effects. Nano Lett. 24(13), 4002–4011 (2024). https://doi.org/10.1021/acs.nanolett.4c00623
  40. L. Chen, M. Ren, J. Zhou, X. Zhou, F. Liu et al., Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles. Proc. Natl. Acad. Sci. U.S.A. 121(33), e2407971121 (2024). https://doi.org/10.1073/pnas.2407971121
  41. J. Zhou, H. Zhang, Q. Qiao, H. Chen, Q. Huang et al., Denoising-autoencoder-facilitated MEMS computational spectrometer with enhanced resolution on a silicon photonic chip. Nat. Commun. 15(1), 10260 (2024). https://doi.org/10.1038/s41467-024-54704-1
  42. Y. Ma, W. Liu, X. Liu, N. Wang, H. Zhang, Review of sensing and actuation technologies–from optical MEMS and nanophotonics to photonic nanosystems. Int. J. Optomechatron. 18(1), 2342279 (2024). https://doi.org/10.1080/15599612.2024.2342279
  43. Z. Ren, B. Dong, Q. Qiao, Subwavelength on-chip light focusing with bigradient all-dielectric metamaterials for dense photonic integration. InfoMat 4(2), e12264 (2022). https://doi.org/10.1002/inf2.12264
  44. Y. Shen, N.C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones et al., Deep learning with coherent nanophotonic circuits. Nat. Photon. 11(7), 441–446 (2017). https://doi.org/10.1038/nphoton.2017.93
  45. F. Brückerhoff-Plückelmann, J. Feldmann, H. Gehring, W. Zhou, C. David Wright et al., Broadband photonic tensor core with integrated ultra-low crosstalk wavelength multiplexers. Nanophotonics 11(17), 4063–4072 (2022). https://doi.org/10.1515/nanoph-2021-0752
  46. B.J. Shastri, A.N. Tait, T. Ferreira de Lima, W.H.P. Pernice, H. Bhaskaran et al., Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15(2), 102–114 (2021). https://doi.org/10.1038/s41566-020-00754-y
  47. H. Zhao, B. Li, H. Li, M. Li, Enabling scalable optical computing in synthetic frequency dimension using integrated cavity acousto-optics. Nat. Commun. 13, 5426 (2022). https://doi.org/10.1038/s41467-022-33132-z
  48. C. Wu, X. Yang, Y. Chen, M. Li, Photonic Bayesian neural network using programmed optical noises. IEEE J. Sel. Top. Quantum Electron. 29(2: Optical computing), 6100606 (2023). https://doi.org/10.1109/JSTQE.2022.3217819
  49. H. Shu, L. Chang, Y. Tao, B. Shen, W. Xie et al., Microcomb-driven silicon photonic systems. Nature 605(7910), 457–463 (2022). https://doi.org/10.1038/s41586-022-04579-3
  50. B. Bai, Q. Yang, H. Shu, L. Chang, F. Yang et al., Microcomb-based integrated photonic processing unit. Nat. Commun. 14, 66 (2023). https://doi.org/10.1038/s41467-022-35506-9
  51. K.Y. Yang, C. Shirpurkar, A.D. White, J. Zang, L. Chang et al., Multi-dimensional data transmission using inverse-designed silicon photonics and microcombs. Nat. Commun. 13(1), 7862 (2022). https://doi.org/10.1038/s41467-022-35446-4
  52. F. Wang, F. Hu, M. Dai, S. Zhu, F. Sun et al., A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding. Nat. Commun. 14(1), 1938 (2023). https://doi.org/10.1038/s41467-023-37623-5
  53. J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li et al., Parallel convolutional processing using an integrated photonic tensor core. Nature 589(7840), 52–58 (2021). https://doi.org/10.1038/s41586-020-03070-1
  54. F. Ashtiani, A.J. Geers, F. Aflatouni, An on-chip photonic deep neural network for image classification. Nature 606(7914), 501–506 (2022). https://doi.org/10.1038/s41586-022-04714-0
  55. X. Xu, M. Tan, B. Corcoran, J. Wu, A. Boes et al., 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589(7840), 44–51 (2021). https://doi.org/10.1038/s41586-020-03063-0
  56. W. Zhou, B. Dong, N. Farmakidis, X. Li, N. Youngblood et al., In-memory photonic dot-product engine with electrically programmable weight banks. Nat. Commun. 14(1), 2887 (2023). https://doi.org/10.1038/s41467-023-38473-x
  57. Y. Zhang, J.B. Chou, J. Li, H. Li, Q. Du et al., Broadband transparent optical phase change materials for high-performance nonvolatile photonics. Nat. Commun. 10(1), 4279 (2019). https://doi.org/10.1038/s41467-019-12196-4
  58. C. Ríos, Q. Du, Y. Zhang, C.-C. Popescu, M.Y. Shalaginov et al., Ultra-compact nonvolatile phase shifter based on electrically reprogrammable transparent phase change materials. PhotoniX 3(1), 26 (2022). https://doi.org/10.1186/s43074-022-00070-4
  59. B. Dong, S. Aggarwal, W. Zhou, U.E. Ali, N. Farmakidis et al., Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photon. 17(12), 1080–1088 (2023). https://doi.org/10.1038/s41566-023-01313-x
  60. Z. Xu, T. Zhou, M. Ma, C. Deng, Q. Dai et al., Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence. Science 384(6692), 202–209 (2024). https://doi.org/10.1126/science.adl1203
  61. J. Cheng, C. Huang, J. Zhang, B. Wu, W. Zhang et al., Multimodal deep learning using on-chip diffractive optics with in situ training capability. Nat. Commun. 15(1), 6189 (2024). https://doi.org/10.1038/s41467-024-50677-3
  62. Z. Xiao, Z. Ren, Y. Zhuge, Z. Zhang, J. Zhou et al., Multimodal in-sensor computing system using integrated silicon photonic convolutional processor. Adv. Sci. 11(47), e2408597 (2024). https://doi.org/10.1002/advs.202408597
  63. X. Liu, Z. Zhang, J. Zhou, W. Liu, G. Zhou et al., Development of photonic in-sensor computing based on a mid-infrared silicon waveguide platform. ACS Nano 18(34), 22938–22948 (2024). https://doi.org/10.1021/acsnano.4c04052
  64. Z. Zhang, X. Guo, and C. Lee, Advances in olfactory augmented virtual reality towards future metaverse applications. Nat. Commun. 15, 6465 (2024). https://doi.org/10.1038/s41467-024-50261-9
  65. J. Zhu, H. Wang, Z. Zhang, Z. Ren, Q. Shi et al., Continuous direct current by charge transportation for next-generation IoT and real-time virtual reality applications. Nano Energy 73, 104760 (2020). https://doi.org/10.1016/j.nanoen.2020.104760
  66. N. Li, C.P. Ho, S. Zhu, Y.H. Fu, Y. Zhu et al., Aluminium nitride integrated photonics: a review. Nanophotonics 10(9), 2347–2387 (2021). https://doi.org/10.1515/nanoph-2021-0130
  67. B. Dong, Q. Shi, T. He, S. Zhu, Z. Zhang et al., Wearable triboelectric/aluminum nitride nano-energy-nano-system with self-sustainable photonic modulation and continuous force sensing. Adv. Sci. 7(15), 1903636 (2020). https://doi.org/10.1002/advs.201903636
  68. M. Zhu, Z. Sun, Z. Zhang, Q. Shi, T. He et al., Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications. Sci. Adv. (2020). https://doi.org/10.1126/sciadv.aaz8693
  69. F. Wen, Z. Zhang, T. He, C. Lee, AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove. Nat. Commun. 12(1), 5378 (2021). https://doi.org/10.1038/s41467-021-25637-w
  70. Z. Zhang, T. He, M. Zhu, Z. Sun, Q. Shi et al., Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications. npj Flex. Electron. 4, 29 (2020). https://doi.org/10.1038/s41528-020-00092-7
  71. B. Dong, Z. Zhang, Q. Shi, J. Wei, Y. Ma et al., Biometrics-protected optical communication enabled by deep learning-enhanced triboelectric/photonic synergistic interface. Sci. Adv. (2022). https://doi.org/10.1126/sciadv.abl9874
  72. Q. Shi, B. Dong, T. He, Progress in wearable electronics/photonics: moving toward the era of artificial intelligence and Internet of Things. InfoMat 2(6), 1131–1162 (2020). https://doi.org/10.1002/inf2.12122
  73. Y. Yang, T. He, P. Ravindran, F. Wen, P. Krishnamurthy et al., All-organic transparent plant e-skin for noninvasive phenotyping. Sci. Adv. (2024). https://doi.org/10.1126/sciadv.adk7488
  74. Y. Luo, M.R. Abidian, J.-H. Ahn, D. Akinwande, A.M. Andrews et al., Technology roadmap for flexible sensors. ACS Nano 17(6), 5211–5295 (2023). https://doi.org/10.1021/acsnano.2c12606
  75. B. Dong, Y. Ma, Z. Ren, C. Lee, Recent progress in nanoplasmonics-based integrated optical micro/nano-systems. J. Phys. D Appl. Phys. 53(21), 213001 (2020). https://doi.org/10.1088/1361-6463/ab77db
  76. X. Guo, L. Wang, Z. Jin, C. Lee, A multifunctional hydrogel with multimodal self-powered sensing capability and stable direct current output for outdoor plant monitoring systems. Nano-Micro Lett. 17(1), 76 (2024). https://doi.org/10.1007/s40820-024-01587-y
  77. Z. Ren, Y. Chang, Y. Ma, K. Shih, B. Dong et al., Leveraging of MEMS technologies for optical metamaterials applications. Adv. Opt. Mater. 8(3), 1900653 (2020). https://doi.org/10.1002/adom.201900653
  78. X. Guo, Z. Sun, Y. Zhu, C. Lee, Zero-biased bionic fingertip E-skin with multimodal tactile perception and artificial intelligence for augmented touch awareness. Adv. Mater. 36(39), e2406778 (2024). https://doi.org/10.1002/adma.202406778
  79. Y. Pang, X. Zhu, T. He, S. Liu, Z. Zhang et al., AI-assisted self-powered vehicle-road integrated electronics for intelligent transportation collaborative perception. Adv. Mater. 36(36), e2404763 (2024). https://doi.org/10.1002/adma.202404763
  80. Q. Lv, J. Qiu, and Q. Wen, The elasticity and piezoelectricity of AlN containing charged vacancies. Int. J. Optomechatronics. 18(1), 2332242 (2024). https://doi.org/10.1080/15599612.2024.2332242
  81. M. Liu, Z. Dai, Y. Zhao, H. Ling, L. Sun et al., Tactile sensing and rendering patch with dynamic and static sensing and haptic feedback for immersive communication. ACS Appl. Mater. Interfaces 16(39), 53207–53219 (2024). https://doi.org/10.1021/acsami.4c11050
  82. Z. Sun, Z. Zhang, C. Lee, A skin-like multimodal haptic interface. Nat. Electron. 6, 941-942 (2024). https://doi.org/10.1038/s41928-023-01093-w
  83. K. Xiao, C. Wan, L. Jiang, X. Chen, M. Antonietti, Bioinspired ionic sensory systems: the successor of electronics. Adv. Mater. 32(31), e2000218 (2020). https://doi.org/10.1002/adma.202000218
  84. D. Li, H. Zhou, Z. Ren, C. Xu, C. Lee, Tailoring light-matter interactions in overcoupled resonator for biomolecule recognition and detection. Nano-Micro Lett. 17(1), 10 (2024). https://doi.org/10.1007/s40820-024-01520-3
  85. J. Zhou, Z. Zhang, B. Dong, Z. Ren, W. Liu et al., Midinfrared spectroscopic analysis of aqueous mixtures using artificial-intelligence-enhanced metamaterial waveguide sensing platform. ACS Nano 17(1), 711–724 (2023). https://doi.org/10.1021/acsnano.2c10163
  86. Z. Ren, Z. Zhang, J. Wei, B. Dong, C. Lee, Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy. Nat. Commun. 13(1), 3859 (2022). https://doi.org/10.1038/s41467-022-31520-z
  87. J. Xu, Z. Ren, B. Dong, X. Liu, C. Wang et al., Nanometer-scale heterogeneous interfacial sapphire wafer bonding for enabling plasmonic-enhanced nanofluidic mid-infrared spectroscopy. ACS Nano 14(9), 12159–12172 (2020). https://doi.org/10.1021/acsnano.0c05794
  88. H. Zhou, Z. Ren, C. Xu, L. Xu, C. Lee, MOF/polymer-integrated multi-hotspot mid-infrared nanoantennas for sensitive detection of CO2 gas. Nano-Micro Lett. 14(1), 207 (2022). https://doi.org/10.1007/s40820-022-00950-1
  89. D. Li, H. Zhou, Z. Chen, Z. Ren, C. Xu et al., Ultrasensitive molecular fingerprint retrieval using strongly detuned overcoupled plasmonic nanoantennas. Adv. Mater. 35(32), e2301787 (2023). https://doi.org/10.1002/adma.202301787
  90. J. Zhu, Z. Ren, C. Lee, Toward healthcare diagnoses by machine-learning-enabled volatile organic compound identification. ACS Nano 15(1), 894–903 (2021). https://doi.org/10.1021/acsnano.0c07464
  91. H. Zhou, Z. Ren, D. Li, C. Xu, X. Mu et al., Dynamic construction of refractive index-dependent vibrations using surface plasmon-phonon polaritons. Nat. Commun. 14(1), 7316 (2023). https://doi.org/10.1038/s41467-023-43127-z
  92. N. Li, C.P. Ho, J. Xue, L.W. Lim, G. Chen et al., A progress review on solid-state LiDAR and nanophotonics-based LiDAR sensors. Laser Photon. Rev. 16(11), 2100511 (2022). https://doi.org/10.1002/lpor.202100511
  93. J. Wei, Y. Chen, Y. Li, W. Li, J. Xie et al., Geometric filterless photodetectors for mid-infrared spin light. Nat. Photon. (2022). https://doi.org/10.1038/s41566-022-01115-7
  94. J. Xie, Z. Ren, J. Wei, W. Liu, J. Zhou et al., Zero-bias long-wave infrared nanoantenna-mediated graphene photodetector for polarimetric and spectroscopic sensing. Adv. Opt. Mater. 11(9), 2202867 (2023). https://doi.org/10.1002/adom.202202867
  95. J. Wei, C. Xu, B. Dong, C.-W. Qiu, C. Lee, Mid-infrared semimetal polarization detectors with configurable polarity transition. Nat. Photon. 15(8), 614–621 (2021). https://doi.org/10.1038/s41566-021-00819-6
Read More
Author biographies are not available.
Download this PDF file
PDF SUPPLEMENTARY FILE
Statistic
Read Counter : 817 Download : 610