Issue

Vol. 18 (2026)

Advanced Design for High-Performance and AI Chips

https://doi.org/10.1007/s40820-025-01850-w
Ying Cao
Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China
Yuejiao Chen
State Key Laboratory for Powder Metallurgy, Central South University, Changsha 410083, People’s Republic of China
Xi Fan
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, People’s Republic of China
Hong Fu
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, People’s Republic of China
Bingang Xu
Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China

Corresponding Author: Bingang Xu

tcxubg@polyu.edu.hk

Nano-Micro Letters, Vol. 18 (2026), Article Number: 13

Abstract

Recent years have witnessed transformative changes brought about by artificial intelligence (AI) techniques with billions of parameters for the realization of high accuracy, proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully. Rapid progress has been made in the field of advanced chips recently, such as the development of photonic computing, the advancement of the quantum processors, the boost of the biomimetic chips, and so on. Designs tactics of the advanced chips can be conducted with elaborated consideration of materials, algorithms, models, architectures, and so on. Though a few reviews present the development of the chips from their unique aspects, reviews in the view of the latest design for advanced and AI chips are few. Here, the newest development is systematically reviewed in the field of advanced chips. First, background and mechanisms are summarized, and subsequently most important considerations for co-design of the software and hardware are illustrated. Next, strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration, after which the design thought for the advanced chips in the future is proposed. Finally, some perspectives are put forward.


Highlights:
1 A comprehensive review focused on the recent advancement of the advanced and artificial intelligence (AI) chip is presented.
2 The design tactics for the enhanced and AI chips can be conducted from a diversity of aspects, with materials, circuit, architecture, and packaging technique taken into considerations, for the pursuit of multimodal data processing abilities, robust reconfigurability, high energy efficiency, and enhanced computing power.
3 A broad outlook on the future considerations of the advanced chip is put forward.

Keywords

Artificial intelligence Advanced chips AI chips Design tactics Review and perspective
Cao, Ying, Yuejiao Chen, Xi Fan, Hong Fu, and Bingang Xu. 2025. “Advanced Design for High-Performance and AI Chips”. Nano-Micro Letters 18 (July):13. https://doi.org/10.1007/s40820-025-01850-w.
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References
  1. S. Ambrogio, P. Narayanan, A. Okazaki, A. Fasoli, C. Mackin et al., An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620(7975), 768–775 (2023). https://doi.org/10.1038/s41586-023-06337-5
  2. L. Gao, J. Lin, L. Wang et al., Machine learning-assisted design of advanced polymeric materials. Acc. Mater. Res. 5(5), 571–584 (2024). https://doi.org/10.1021/accountsmr.3c00288
  3. J. Benavides-Hernández, F. Dumeignil, From characterization to discovery: artificial intelligence, machine learning and high-throughput experiments for heterogeneous catalyst design. ACS Catal. 14(15), 11749–11779 (2024). https://doi.org/10.1021/acscatal.3c06293
  4. Y. Fu, A. Howard, C. Zeng, Y. Chen, P. Gao et al., Physics-guided continual learning for predicting emerging aqueous organic redox flow battery material performance. ACS Energy Lett. 9(6), 2767–2774 (2024). https://doi.org/10.1021/acsenergylett.4c00493
  5. P. Akbari, M. Zamani, A. Mostafaei, Machine learning prediction of mechanical properties in metal additive manufacturing. Addit. Manuf. 91, 104320 (2024). https://doi.org/10.1016/j.addma.2024.104320
  6. Q. Liu, W. Chen, V. Yakubov, J.J. Kruzic, C.H. Wang et al., Interpretable machine learning approach for exploring process-structure-property relationships in metal additive manufacturing. Addit. Manuf. 85, 104187 (2024). https://doi.org/10.1016/j.addma.2024.104187
  7. J. Li, M. Zhou, H.-H. Wu, L. Wang, J. Zhang et al., Machine learning-assisted property prediction of solid-state electrolyte. Adv. Energy Mater. 14(20), 2304480 (2024). https://doi.org/10.1002/aenm.202304480
  8. X. Zhou, C. Xu, X. Guo, P. Apostol, A. Vlad et al., Computational and machine learning-assisted discovery and experimental validation of conjugated sulfonamide cathodes for lithium-ion batteries. Adv. Energy Mater. 15, 2401658 (2024). https://doi.org/10.1002/aenm.202401658
  9. E. Alibagheri, A. Ranjbar, M. Khazaei, T.D. Kühne, S.M. Vaez Allaei, Remarkable optoelectronic characteristics of synthesizable square-octagon haeckelite structures: machine learning materials discovery. Adv. Funct. Mater. 34(27), 2470150 (2024). https://doi.org/10.1002/adfm.202470150
  10. T. Jing, B. Xu, Y. Yang, M. Li, Y. Gao, Organogel electrode enables highly transparent and stretchable triboelectric nanogenerators of high power density for robust and reliable energy harvesting. Nano Energy 78, 105373 (2020). https://doi.org/10.1016/j.nanoen.2020.105373
  11. Y. Liu, B. Xie, Q. Hu, R. Zhao, Q. Zheng et al., Regulating the Helmholtz plane by trace polarity additive for long-life Zn ion batteries. Energy Storage Mater. 66, 103202 (2024). https://doi.org/10.1016/j.ensm.2024.103202
  12. J. Wen, B. Xu, J. Zhou, Toward flexible and wearable embroidered supercapacitors from cobalt phosphides-decorated conductive fibers. Nano-Micro Lett. 11(1), 89 (2019). https://doi.org/10.1007/s40820-019-0321-x
  13. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
  14. G.E. Dahl, D. Yu, L. Deng, A. Acero, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012). https://doi.org/10.1109/TASL.2011.2134090
  15. W.-N. Hsu, B. Bolte, Y.H. Tsai, K. Lakhotia, R. Salakhutdinov et al., HuBERT: self-supervised speech representation learning by masked prediction of hidden units. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 3451–3460 (2021). https://doi.org/10.1109/TASLP.2021.3122291
  16. J. Wu, Y. Guo, C. Deng, A. Zhang, H. Qiao et al., An integrated imaging sensor for aberration-corrected 3D photography. Nature 612(7938), 62–71 (2022). https://doi.org/10.1038/s41586-022-05306-8
  17. A. Suleiman, Z. Zhang, L. Carlone, S. Karaman, V. Sze, Navion: a 2-mW fully integrated real-time visual-inertial odometry accelerator for autonomous navigation of nano drones. IEEE J. Solid State Circuits 54(4), 1106–1119 (2019). https://doi.org/10.1109/JSSC.2018.2886342
  18. J. Bai, S. Lian, Z. Liu, K. Wang, D. Liu, Smart guiding glasses for visually impaired people in indoor environment. IEEE Trans. Consum. Electron. 63(3), 258–266 (2017). https://doi.org/10.1109/TCE.2017.014980
  19. T. Starner, Project glass: an extension of the self. IEEE Pervasive Comput. 12(2), 14–16 (2013). https://doi.org/10.1109/MPRV.2013.35
  20. J. Si, P. Zhang, C. Zhao, D. Lin, L. Xu et al., A carbon-nanotube-based tensor processing unit. Nat. Electron. 7(8), 684–693 (2024). https://doi.org/10.1038/s41928-024-01211-2
  21. X. Lin, Y. Rivenson, N.T. Yardimci, M. Veli, Y. Luo et al., All-optical machine learning using diffractive deep neural networks. Science 361(6406), 1004–1008 (2018). https://doi.org/10.1126/science.aat8084
  22. 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
  23. G. Wetzstein, A. Ozcan, S. Gigan, S. Fan, D. Englund et al., Inference in artificial intelligence with deep optics and photonics. Nature 588(7836), 39–47 (2020). https://doi.org/10.1038/s41586-020-2973-6
  24. 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
  25. 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
  26. F. Zangeneh-Nejad, D.L. Sounas, A. Alù, R. Fleury, Analogue computing with metamaterials. Nat. Rev. Mater. 6(3), 207–225 (2021). https://doi.org/10.1038/s41578-020-00243-2
  27. 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
  28. G. Genty, L. Salmela, J.M. Dudley, D. Brunner, A. Kokhanovskiy et al., Machine learning and applications in ultrafast photonics. Nat. Photonics 15(2), 91–101 (2021). https://doi.org/10.1038/s41566-020-00716-4
  29. S. Molesky, Z. Lin, A.Y. Piggott, W. Jin, J. Vucković et al., Inverse design in nanophotonics. Nat. Photonics 12(11), 659–670 (2018). https://doi.org/10.1038/s41566-018-0246-9
  30. A.M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe et al., Experimental neural network enhanced quantum tomography. NPJ Quantum Inf. 6, 20 (2020). https://doi.org/10.1038/s41534-020-0248-6
  31. J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria et al., Nanophotonic p simulation and inverse design using artificial neural networks. Sci. Adv. 4(6), eaar4206 (2018). https://doi.org/10.1126/sciadv.aar4206
  32. T.W. Hughes, M. Minkov, I.A.D. Williamson, S. Fan, Adjoint method and inverse design for nonlinear nanophotonic devices. ACS Photonics 5(12), 4781–4787 (2018). https://doi.org/10.1021/acsphotonics.8b01522
  33. A.Y. Piggott, J. Lu, K.G. Lagoudakis, J. Petykiewicz, T.M. Babinec et al., Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer. Nat. Photonics 9(6), 374–377 (2015). https://doi.org/10.1038/nphoton.2015.69
  34. Z. Yang, W. Yue, C. Liu, Y. Tao, P.J. Tiw et al., Fully hardware memristive neuromorphic computing enabled by the integration of trainable dendritic neurons and high-density RRAM chip. Adv. Funct. Mater. 34(44), 2405618 (2024). https://doi.org/10.1002/adfm.202405618
  35. Z. Xue, T. Zhou, Z. Xu, S. Yu, Q. Dai et al., Fully forward mode training for optical neural networks. Nature 632(8024), 280–286 (2024). https://doi.org/10.1038/s41586-024-07687-4
  36. W. Wang, Y. Wang, F. Yin, H. Niu, Y.-K. Shin et al., Tailoring classical conditioning behavior in TiO2 nanowires: ZnO QDs-based optoelectronic memristors for neuromorphic hardware. Nano-Micro Lett. 16(1), 133 (2024). https://doi.org/10.1007/s40820-024-01338-z
  37. M. Yao, O. Richter, G. Zhao, N. Qiao, Y. Xing et al., Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat. Commun. 15(1), 4464 (2024). https://doi.org/10.1038/s41467-024-47811-6
  38. 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
  39. Y.J. Choi, D.G. Roe, Z. Li, Y.Y. Choi, B. Lim et al., Weight-reconfigurable neuromorphic computing systems for analog signal integration. Adv. Funct. Mater. 34(33), 2316664 (2024). https://doi.org/10.1002/adfm.202316664
  40. D. Lu, Y. Chen, Z. Lu, L. Ma, Q. Tao et al., Monolithic three-dimensional tier-by-tier integration via van der Waals lamination. Nature 630(8016), 340–345 (2024). https://doi.org/10.1038/s41586-024-07406-z
  41. 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
  42. Y. Bu, T. Xu, S. Geng, S. Fan, Q. Li et al., Ferroelectrics-electret synergetic organic artificial synapses with single-polarity driven dynamic reconfigurable modulation. Adv. Funct. Mater. 33(20), 2213741 (2023). https://doi.org/10.1002/adfm.202213741
  43. B. Dong, F. Brückerhoff-Plückelmann, L. Meyer, J. Dijkstra, I. Bente et al., Partial coherence enhances parallelized photonic computing. Nature 632(8023), 55–62 (2024). https://doi.org/10.1038/s41586-024-07590-y
  44. G. Indiveri, R. Douglas, Neuromorphic vision sensors. Science 288(5469), 1189–1190 (2000). https://doi.org/10.1126/science.288.5469.1189
  45. P. Lichtsteiner, C. Posch, T. Delbruck, A 128× 128 120 dB 15 $mu$s latency asynchronous temporal contrast vision sensor. IEEE J. Solid State Circuits 43(2), 566–576 (2008). https://doi.org/10.1109/JSSC.2007.914337
  46. G. Gallego, T. Delbruck, G. Orchard, C. Bartolozzi, B. Taba et al., Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 154–180 (2022). https://doi.org/10.1109/tpami.2020.3008413
  47. Y. van de Burgt, A. Melianas, S.T. Keene, G. Malliaras, A. Salleo, Organic electronics for neuromorphic computing. Nat. Electron. 1(7), 386–397 (2018). https://doi.org/10.1038/s41928-018-0103-3
  48. M.E. Beck, A. Shylendra, V.K. Sangwan, S. Guo, W.A. Gaviria Rojas et al., Spiking neurons from tunable Gaussian heterojunction transistors. Nat. Commun. 11(1), 1565 (2020). https://doi.org/10.1038/s41467-020-15378-7
  49. G.-T. Go, Y. Lee, D.-G. Seo, T.-W. Lee, Organic neuroelectronics: from neural interfaces to neuroprosthetics. Adv. Mater. 34(45), e2201864 (2022). https://doi.org/10.1002/adma.202201864
  50. C. Qian, Y. Choi, S. Kim, S. Kim, Y.J. Choi et al., Risk-perceptional and feedback-controlled response system based on NO2-detecting artificial sensory synapse. Adv. Funct. Mater. 32(18), 2112490 (2022). https://doi.org/10.1002/adfm.202112490
  51. K. Roy, A. Jaiswal, P. Panda, Towards spike-based machine intelligence with neuromorphic computing. Nature 575(7784), 607–617 (2019). https://doi.org/10.1038/s41586-019-1677-2
  52. 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
  53. J.H.R. Maunsell, Neuronal mechanisms of visual attention. Annu. Rev. Vis. Sci. 1, 373–391 (2015). https://doi.org/10.1146/annurev-vision-082114-035431
  54. Q. Liu, S. Gao, L. Xu, W. Yue, C. Zhang et al., Nanostructured perovskites for nonvolatile memory devices. Chem. Soc. Rev. 51(9), 3341–3379 (2022). https://doi.org/10.1039/d1cs00886b
  55. M. Chen, M. Sun, H. Bao, Y. Hu, B. Bao, Flux–charge analysis of two-memristor-based chua’s circuit: dimensionality decreasing model for detecting extreme multistability. IEEE Trans. Ind. Electron. 67(3), 2197–2206 (2019). https://doi.org/10.1109/TIE.2019.2907444
  56. N. Fei, Z. Lu, Y. Gao, G. Yang, Y. Huo et al., Towards artificial general intelligence via a multimodal foundation model. Nat. Commun. 13(1), 3094 (2022). https://doi.org/10.1038/s41467-022-30761-2
  57. H. Zhou, J. Dong, J. Cheng, W. Dong, C. Huang et al., Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11(1), 30 (2022). https://doi.org/10.1038/s41377-022-00717-8
  58. 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. Photonics 15(2), 102–114 (2021). https://doi.org/10.1038/s41566-020-00754-y
  59. 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
  60. Z. Wang, S. Joshi, S. Savel’ev, W. Song, R. Midya et al., Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1(2), 137–145 (2018). https://doi.org/10.1038/s41928-018-0023-2
  61. E. Peterson, A. Lavin, Physical computing for materials acceleration platforms. Matter 5(11), 3586–3596 (2022). https://doi.org/10.1016/j.matt.2022.09.022
  62. K. Hippalgaonkar, Q. Li, X. Wang, J.W. Fisher III., J. Kirkpatrick et al., Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics. Nat. Rev. Mater. 8(4), 241–260 (2023). https://doi.org/10.1038/s41578-022-00513-1
  63. H. Huang, O. Zheng, D. Wang, J. Yin, Z. Wang et al., ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. Int. J. Oral Sci. 15(1), 29 (2023). https://doi.org/10.1038/s41368-023-00239-y
  64. B. Meskó, The impact of multimodal large language models on health care’s future. J. Med. Internet Res. 25, e52865 (2023). https://doi.org/10.2196/52865
  65. M. Moor, O. Banerjee, Z.S.H. Abad, H.M. Krumholz, J. Leskovec et al., Foundation models for generalist medical artificial intelligence. Nature 616(7956), 259–265 (2023). https://doi.org/10.1038/s41586-023-05881-4
  66. X. Wang, G. Chen, G. Qian, P. Gao, X.-Y. Wei et al., Large-scale multi-modal pre-trained models: a comprehensive survey. Mach. Intell. Res. 20(4), 447–482 (2023). https://doi.org/10.1007/s11633-022-1410-8
  67. M.A. Zidan, J.P. Strachan, W.D. Lu, The future of electronics based on memristive systems. Nat. Electron. 1(1), 22–29 (2018). https://doi.org/10.1038/s41928-017-0006-8
  68. B. Yan, Y. Yang, R. Huang, Memristive dynamics enabled neuromorphic computing systems. Sci. China Inf. Sci. 66(10), 200401 (2023). https://doi.org/10.1007/s11432-023-3739-0
  69. A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, E. Eleftheriou, Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15(7), 529–544 (2020). https://doi.org/10.1038/s41565-020-0655-z
  70. S. Ambrogio, P. Narayanan, H. Tsai, R.M. Shelby, I. Boybat et al., Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558(7708), 60–67 (2018). https://doi.org/10.1038/s41586-018-0180-5
  71. R. Khaddam-Aljameh, M. Stanisavljevic, J. Fornt Mas, G. Karunaratne, M. Brandli et al., HERMES-core: a 1.59-TOPS/mm2 PCM on 14-nm CMOS in-memory compute core using 300-ps/LSB linearized CCO-based ADCs. IEEE J. Solid State Circuits 57(4), 1027–1038 (2022). https://doi.org/10.1109/jssc.2022.3140414
  72. P. Yao, H. Wu, B. Gao, J. Tang, Q. Zhang et al., Fully hardware-implemented memristor convolutional neural network. Nature 577(7792), 641–646 (2020). https://doi.org/10.1038/s41586-020-1942-4
  73. W. Wan, R. Kubendran, C. Schaefer, S.B. Eryilmaz, W. Zhang et al., A compute-in-memory chip based on resistive random-access memory. Nature 608(7923), 504–512 (2022). https://doi.org/10.1038/s41586-022-04992-8
  74. S. Jain, H. Tsai, C.-T. Chen, R. Muralidhar, I. Boybat et al., A heterogeneous and programmable compute-In-memory accelerator architecture for analog-AI using dense 2-D mesh. IEEE Trans. VLSI Syst. 31(1), 114–127 (2023). https://doi.org/10.1109/tvlsi.2022.3221390
  75. O. Krestinskaya, K.N. Salama, A.P. James, Learning in memristive neural network architectures using analog backpropagation circuits. IEEE Trans. Circuits Syst. I Regul. Pap. 66(2), 719–732 (2019). https://doi.org/10.1109/TCSI.2018.2866510
  76. M. Giordano, G. Cristiano, K. Ishibashi, S. Ambrogio, H. Tsai et al., Analog-to-digital conversion with reconfigurable function mapping for neural networks activation function acceleration. IEEE J. Emerg. Sel. Top. Circuits Syst. 9(2), 367–376 (2019). https://doi.org/10.1109/JETCAS.2019.2911537
  77. J. Hochstetter, R. Zhu, A. Loeffler, A. Diaz-Alvarez, T. Nakayama et al., Avalanches and edge-of-chaos learning in neuromorphic nanowire networks. Nat. Commun. 12(1), 4008 (2021). https://doi.org/10.1038/s41467-021-24260-z
  78. W. Schultz, A. Dickinson, Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000). https://doi.org/10.1146/annurev.neuro.23.1.473
  79. R.A. Poldrack, J. Clark, E.J. Paré-Blagoev, D. Shohamy, J. Creso Moyano et al., Interactive memory systems in the human brain. Nature 414(6863), 546–550 (2001). https://doi.org/10.1038/35107080
  80. Z. Wang, C. Li, W. Song, M. Rao, D. Belkin et al., Reinforcement learning with analogue memristor arrays. Nat. Electron. 2(3), 115–124 (2019). https://doi.org/10.1038/s41928-019-0221-6
  81. J.H. Baek, K.J. Kwak, S.J. Kim, J. Kim, J.Y. Kim et al., Two-terminal lithium-mediated artificial synapses with enhanced weight modulation for feasible hardware neural networks. Nano-Micro Lett. 15(1), 69 (2023). https://doi.org/10.1007/s40820-023-01035-3
  82. K. He, Y. Liu, J. Yu, X. Guo, M. Wang et al., Artificial neural pathway based on a memristor synapse for optically mediated motion learning. ACS Nano 16(6), 9691–9700 (2022). https://doi.org/10.1021/acsnano.2c03100
  83. T. Zhou, X. Lin, J. Wu, Y. Chen, H. Xie et al., Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics 15(5), 367–373 (2021). https://doi.org/10.1038/s41566-021-00796-w
  84. T. Fu, Y. Zang, Y. Huang, Z. Du, H. Huang et al., Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14(1), 70 (2023). https://doi.org/10.1038/s41467-022-35772-7
  85. E. Goi, X. Chen, Q. Zhang, B.P. Cumming, S. Schoenhardt et al., Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl. 10(1), 40 (2021). https://doi.org/10.1038/s41377-021-00483-z
  86. H. Zhang, M. Gu, X.D. Jiang, J. Thompson, H. Cai et al., An optical neural chip for implementing complex-valued neural network. Nat. Commun. 12, 457 (2021). https://doi.org/10.1038/s41467-020-20719-7
  87. T. Wang, S.-Y. Ma, L.G. Wright, T. Onodera, B.C. Richard et al., An optical neural network using less than 1 photon per multiplication. Nat. Commun. 13(1), 123 (2022). https://doi.org/10.1038/s41467-021-27774-8
  88. Z. Wang, G. Hu, X. Wang, X. Ding, K. Zhang et al., Single-layer spatial analog meta-processor for imaging processing. Nat. Commun. 13(1), 2188 (2022). https://doi.org/10.1038/s41467-022-29732-4
  89. J. Li, D. Mengu, N.T. Yardimci, Y. Luo, X. Li et al., Spectrally encoded single-pixel machine vision using diffractive networks. Sci. Adv. 7(13), eabd7690 (2021). https://doi.org/10.1126/sciadv.abd7690
  90. M.S.S. Rahman, J. Li, D. Mengu, Y. Rivenson, A. Ozcan, Ensemble learning of diffractive optical networks. Light Sci. Appl. 10(1), 14 (2021). https://doi.org/10.1038/s41377-020-00446-w
  91. J. Feldmann, N. Youngblood, C.D. Wright, H. Bhaskaran, W.P. Pernice, All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569(7755), 208–214 (2019). https://doi.org/10.1038/s41586-019-1157-8
  92. W. Shi, Z. Huang, H. Huang, C. Hu, M. Chen et al., LOEN: lensless opto-electronic neural network empowered machine vision. Light Sci. Appl. 11(1), 121 (2022). https://doi.org/10.1038/s41377-022-00809-5
  93. J. Chang, V. Sitzmann, X. Dun, W. Heidrich, G. Wetzstein, Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep. 8(1), 12324 (2018). https://doi.org/10.1038/s41598-018-30619-y
  94. J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot et al., Reinforcement learning in a large-scale photonic recurrent neural network. Optica 5(6), 756 (2018). https://doi.org/10.1364/optica.5.000756
  95. A. Silva, F. Monticone, G. Castaldi, V. Galdi, A. Alù et al., Performing mathematical operations with metamaterials. Science 343(6167), 160–163 (2014). https://doi.org/10.1126/science.1242818
  96. Y. Shen, N.C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones et al., Deep learning with coherent nanophotonic circuits. Nat. Photonics 11(7), 441–446 (2017). https://doi.org/10.1038/nphoton.2017.93
  97. X. Yuan, Y. Wang, Z. Xu, T. Zhou, L. Fang, Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning. Nat. Commun. 14(1), 7110 (2023). https://doi.org/10.1038/s41467-023-42984-y
  98. T. Zhou, W. Wu, J. Zhang, S. Yu, L. Fang, Ultrafast dynamic machine vision with spatiotemporal photonic computing. Sci. Adv. 9(23), eadg4391 (2023). https://doi.org/10.1126/sciadv.adg4391
  99. Z. Xu, X. Yuan, T. Zhou, L. Fang, A multichannel optical computing architecture for advanced machine vision. Light Sci. Appl. 11(1), 255 (2022). https://doi.org/10.1038/s41377-022-00945-y
  100. S. Neyens, O.K. Zietz, T.F. Watson, F. Luthi, A. Nethwewala et al., Probing single electrons across 300-mm spin qubit wafers. Nature 629(8010), 80–85 (2024). https://doi.org/10.1038/s41586-024-07275-6
  101. D. Wecker, B. Bauer, B.K. Clark, M.B. Hastings, M. Troyer, Gate-count estimates for performing quantum chemistry on small quantum computers. Phys. Rev. A 90(2), 022305 (2014). https://doi.org/10.1103/physreva.90.022305
  102. M. Brauns, S.V. Amitonov, P.-C. Spruijtenburg, F.A. Zwanenburg, Palladium gates for reproducible quantum dots in silicon. Sci. Rep. 8(1), 5690 (2018). https://doi.org/10.1038/s41598-018-24004-y
  103. J.P. Dodson, N. Holman, B. Thorgrimsson, S.F. Neyens, E.R. MacQuarrie et al., Fabrication process and failure analysis for robust quantum dots in silicon. Nanotechnology 31(50), 505001 (2020). https://doi.org/10.1088/1361-6528/abb559
  104. M.M. Shulaker, G. Hills, R.S. Park, R.T. Howe, K. Saraswat et al., Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature 547(7661), 74–78 (2017). https://doi.org/10.1038/nature22994
  105. K. Zhu, S. Pazos, F. Aguirre, Y. Shen, Y. Yuan et al., Hybrid 2D-CMOS microchips for memristive applications. Nature 618(7963), 57–62 (2023). https://doi.org/10.1038/s41586-023-05973-1
  106. S. Kim, J. Seo, J. Choi, H. Yoo, Vertically integrated electronics: new opportunities from emerging materials and devices. Nano-Micro Lett. 14(1), 201 (2022). https://doi.org/10.1007/s40820-022-00942-1
  107. J. Jiang, K. Parto, W. Cao, K. Banerjee, Ultimate monolithic-3D integration with 2D materials: rationale, prospects, and challenges. IEEE J. Electron Devices Soc. 7, 878–887 (2019). https://doi.org/10.1109/JEDS.2019.2925150
  108. L. Tong, J. Wan, K. Xiao, J. Liu, J. Ma et al., Heterogeneous complementary field-effect transistors based on silicon and molybdenum disulfide. Nat. Electron. 6(1), 37–44 (2022). https://doi.org/10.1038/s41928-022-00881-0
  109. W. Meng, F. Xu, Z. Yu, T. Tao, L. Shao et al., Three-dimensional monolithic micro-LED display driven by atomically thin transistor matrix. Nat. Nanotechnol. 16(11), 1231–1236 (2021). https://doi.org/10.1038/s41565-021-00966-5
  110. 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
  111. D. Jayachandran, R. Pendurthi, M.U.K. Sadaf, N.U. Sakib, A. Pannone et al., Three-dimensional integration of two-dimensional field-effect transistors. Nature 625(7994), 276–281 (2024). https://doi.org/10.1038/s41586-023-06860-5
  112. J. Tang, Q. Wang, Z. Wei, C. Shen, X. Lu et al., Vertical integration of 2D building blocks for all-2D electronics. Adv. Electron. Mater. 6(12), 2000550 (2020). https://doi.org/10.1002/aelm.202000550
  113. Y. Liu, Y. Huang, X. Duan, Van der waals integration before and beyond two-dimensional materials. Nature 567(7748), 323–333 (2019). https://doi.org/10.1038/s41586-019-1013-x
  114. H. Zhang, H. Zeng, A. Priimagi, O. Ikkala, Viewpoint: Pavlovian materials-functional biomimetics inspired by classical conditioning. Adv. Mater. 32(20), e1906619 (2020). https://doi.org/10.1002/adma.201906619
  115. M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015). https://doi.org/10.1126/science.aaa8415
  116. D. Woods, T.J. Naughton, Photonic neural networks. Nat. Phys. 8(4), 257–259 (2012). https://doi.org/10.1038/nphys2283
  117. D.A.B. Miller, Device requirements for optical interconnects to silicon chips. Proc. IEEE 97(7), 1166–1185 (2009). https://doi.org/10.1109/JPROC.2009.2014298
  118. D.A.B. Miller, Attojoule optoelectronics for low-energy information processing and communications. J. Light. Technol. 35(3), 346–396 (2017). https://doi.org/10.1109/JLT.2017.2647779
  119. D.A.B. Miller, Waves, modes, communications, and optics: a tutorial. Adv. Opt. Photon. 11(3), 679 (2019). https://doi.org/10.1364/aop.11.000679
  120. J. Wang, J.-Y. Yang, I.M. Fazal, N. Ahmed, Y. Yan et al., Terabit free-space data transmission employing orbital angular momentum multiplexing. Nat. Photonics 6(7), 488–496 (2012). https://doi.org/10.1038/nphoton.2012.138
  121. D.J. Richardson, J.M. Fini, L.E. Nelson, Space-division multiplexing in optical fibres. Nat. Photonics 7(5), 354–362 (2013). https://doi.org/10.1038/nphoton.2013.94
  122. N. Bozinovic, Y. Yue, Y. Ren, M. Tur, P. Kristensen et al., Terabit-scale orbital angular momentum mode division multiplexing in fibers. Science 340(6140), 1545–1548 (2013). https://doi.org/10.1126/science.1237861
  123. R. Ryf, S. Randel, A.H. Gnauck, C. Bolle, A. Sierra et al., Mode-division multiplexing over 96 km of few-mode fiber using coherent 6 × 6 MIMO processing. J. Lightwave Technol. 30(4), 521–531 (2012). https://doi.org/10.1109/jlt.2011.2174336
  124. R.G.H. van Uden, R.A. Correa, E.A. Lopez, F.M. Huijskens, C. Xia et al., Ultra-high-density spatial division multiplexing with a few-mode multicore fibre. Nat. Photonics 8(11), 865–870 (2014). https://doi.org/10.1038/nphoton.2014.243
  125. J.M. Kahn, D.A.B. Miller, Communications expands its space. Nat. Photonics 11(1), 5–8 (2017). https://doi.org/10.1038/nphoton.2016.256
  126. G. Rademacher, B.J. Puttnam, R.S. Luís, T.A. Eriksson, N.K. Fontaine et al., Peta-bit-per-second optical communications system using a standard cladding diameter 15-mode fiber. Nat. Commun. 12(1), 4238 (2021). https://doi.org/10.1038/s41467-021-24409-w
  127. L.H. Gabrielli, D. Liu, S.G. Johnson, M. Lipson, On-chip transformation optics for multimode waveguide bends. Nat. Commun. 3, 1217 (2012). https://doi.org/10.1038/ncomms2232
  128. L.-W. Luo, N. Ophir, C.P. Chen, L.H. Gabrielli, C.B. Poitras et al., WDM-compatible mode-division multiplexing on a silicon chip. Nat. Commun. 5, 3069 (2014). https://doi.org/10.1038/ncomms4069
  129. S.A. Miller, Y.-C. Chang, C.T. Phare, M.C. Shin, M. Zadka et al., Large-scale optical phased array using a low-power multi-pass silicon photonic platform. Optica 7(1), 3 (2020). https://doi.org/10.1364/optica.7.000003
  130. L.F. Frellsen, Y. Ding, O. Sigmund, L.H. Frandsen, Topology optimized mode multiplexing in silicon-on-insulator photonic wire waveguides. Opt. Express 24(15), 16866–16873 (2016). https://doi.org/10.1364/OE.24.016866
  131. W. Chang, L. Lu, X. Ren, D. Li, Z. Pan et al., Ultra-compact mode (de multiplexer based on subwavelength asymmetric Y-junction. Opt. Express 26(7), 8162–8170 (2018). https://doi.org/10.1364/OE.26.008162
  132. Y. Tong, W. Zhou, X. Wu, H.K. Tsang, Efficient mode multiplexer for few-mode fibers using integrated silicon-on-insulator waveguide grating coupler. IEEE J. Quantum Electron. 56(1), 8400107 (2020). https://doi.org/10.1109/JQE.2019.2950126
  133. H. Hu, F. Da Ros, M. Pu, F. Ye, K. Ingerslev et al., Single-source chip-based frequency comb enabling extreme parallel data transmission. Nat. Photonics 12(8), 469–473 (2018). https://doi.org/10.1038/s41566-018-0205-5
  134. Y. Han, G. Huang, S. Song, L. Yang, H. Wang et al., Dynamic neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7436–7456 (2021). https://doi.org/10.1109/TPAMI.2021.3117837
  135. J.A. Ang, D.J. Mountain, New horizons for high-performance computing. Computer 55(12), 156–162 (2022). https://doi.org/10.1109/MC.2022.3200859
  136. J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre et al., Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588(7839), 604–609 (2020). https://doi.org/10.1038/s41586-020-03051-4
  137. A. Sludds, S. Bandyopadhyay, Z. Chen, Z. Zhong, J. Cochrane et al., Delocalized photonic deep learning on the Internet’s edge. Science 378(6617), 270–276 (2022). https://doi.org/10.1126/science.abq8271
  138. L.G. Wright, T. Onodera, M.M. Stein, T. Wang, D.T. Schachter et al., Deep physical neural networks trained with backpropagation. Nature 601(7894), 549–555 (2022). https://doi.org/10.1038/s41586-021-04223-6
  139. W. Ma, Z. Liu, Z.A. Kudyshev, A. Boltasseva, W. Cai et al., Deep learning for the design of photonic structures. Nat. Photonics 15(2), 77–90 (2021). https://doi.org/10.1038/s41566-020-0685-y
  140. N. Mohammadi Estakhri, B. Edwards, N. Engheta, Inverse-designed metastructures that solve equations. Science 363(6433), 1333–1338 (2019). https://doi.org/10.1126/science.aaw2498
  141. A. McNamara, A. Treuille, Z. Popović, J. Stam, Fluid control using the adjoint method. ACM Trans. Graph. 23(3), 449–456 (2004). https://doi.org/10.1145/1015706.1015744
  142. X. Wang, P. Xie, B. Chen, X. Zhang, Chip-based high-dimensional optical neural network. Nano-Micro Lett. 14(1), 221 (2022). https://doi.org/10.1007/s40820-022-00957-8
  143. J. Moran, R. Desimone, Selective attention gates visual processing in the extrastriate cortex. Science 229(4715), 782–784 (1985). https://doi.org/10.1126/science.4023713
  144. T. Moore, M. Zirnsak, Neural mechanisms of selective visual attention. Annu. Rev. Psychol. 68, 47–72 (2017). https://doi.org/10.1146/annurev-psych-122414-033400
  145. X. Duan, Z. Cao, K. Gao, W. Yan, S. Sun et al., Memristor-based neuromorphic chips. Adv. Mater. 36(14), 2310704 (2024). https://doi.org/10.1002/adma.202310704
  146. I. Oguz, M. Yildirim, J.L. Hsieh, N.U. Dinc, C. Moser et al., Resource-efficient photonic networks for next-generation AI computing. Light Sci. Appl. 14(1), 34 (2025). https://doi.org/10.1038/s41377-024-01717-6
  147. D. Wang, Y. Nie, G. Hu, H.K. Tsang, C. Huang, Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS. Nat. Commun. 15(1), 10841 (2024). https://doi.org/10.1038/s41467-024-55172-3
  148. T. Günkel, J. Alcalà, A. Fernández, A. Barrera, L. Balcells et al., Field-induced phase transitions in cuprate superconductors for cryogenic in-memory computing. Small 21(14), e2411908 (2025). https://doi.org/10.1002/smll.202411908
  149. 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
  150. S. Wan, K. Qu, Y. Shi, Z. Li, Z. Wang et al., Multidimensional encryption by chip-integrated metasurfaces. ACS Nano 18(28), 18693–18700 (2024). https://doi.org/10.1021/acsnano.4c05724
  151. S.-A. Guo, Y.-K. Wu, J. Ye, L. Zhang, W.-Q. Lian et al., A site-resolved two-dimensional quantum simulator with hundreds of trapped ions. Nature 630(8017), 613–618 (2024). https://doi.org/10.1038/s41586-024-07459-0
  152. C. Wang, Z. Chen, C.L.J. Chan, Z.-A. Wan, W. Ye et al., Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor arrays. Nat. Electron. 7(2), 157–167 (2024). https://doi.org/10.1038/s41928-023-01107-7
  153. D. Kumar, H. Li, D.D. Kumbhar, M.K. Rajbhar, U.K. Das et al., Highly efficient back-end-of-line compatible flexible Si-based optical memristive crossbar array for edge neuromorphic physiological signal processing and bionic machine vision. Nano-Micro Lett. 16(1), 238 (2024). https://doi.org/10.1007/s40820-024-01456-8
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