• Niu, H. et al. Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence. InfoMat 5, e12412 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Kong, J. The imitation, surpassing, and challenge of artificial perception to natural perception. J. Hum. Cogn. 8, 8–16 (2024).

    Article 

    Google Scholar
     

  • Deng, C., Ji, X., Rainey, C., Zhang, J. & Lu, W. Integrating machine learning with human knowledge. iScience 23, 101656 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, J. & Carayon, P. Health Care 4.0: a vision for smart and connected health care. IISE Trans. Healthc. Syst. Eng. https://doi.org/10.1080/24725579.2021.1884627 (2021).

  • Huang, S.-C. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. (2020).

  • Sakib, S., Fouda, M. M. & Fadlullah, Z. M. A rigorous analysis of biomedical edge computing: an arrhythmia classification use-case leveraging deep learning. In 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS) 136–141 (IEEE, BALI, Indonesia, 2021).

  • Meuser, T. et al. Revisiting Edge AI: opportunities and challenges. IEEE Internet Comput. 28, 49–59 (2024).

    Article 

    Google Scholar
     

  • Leng, J. et al. Unlocking the power of industrial artificial intelligence towards Industry 5.0: insights, pathways, and challenges. J. Manuf. Syst. 73, 349–363 (2024).

    Article 

    Google Scholar
     

  • Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, Z., Wang, L. & Lee, C. Recent advances in artificial intelligence sensors. Adv. Sens. Res. 2, 2200072 (2023).

    Article 

    Google Scholar
     

  • Wang, H. et al. Recent progress on artificial intelligence-enhanced multimodal sensors integrated devices and systems. J. Semicond. 46, 011610 (2025).

    Article 
    ADS 

    Google Scholar
     

  • Katmah, R., Shehhi, A. A., Jelinek, H. F., Hulleck, A. A. & Khalaf, K. A systematic review of gait analysis in the context of multimodal sensing fusion and AI. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 4189–4202 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Narkhede, P. et al. Gas detection and identification using multimodal artificial intelligence based sensor fusion. Appl. Syst. Innov. 4, 3 (2021).

    Article 

    Google Scholar
     

  • Yu, K., Kim, S. & Choi, J. R. Trends and challenges in computing-in-memory for neural network model: a review from device design to application-side optimization. IEEE Access 12, 186679–186702 (2024).

    Article 

    Google Scholar
     

  • Passian, A. & Imam, N. Nanosystems, edge computing, and the next generation computing systems. Sensors 19, 4048 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, D. et al. In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing. Nat. Commun. 13, 5223 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, H. S. et al. Efficient defect identification via oxide memristive crossbar array based morphological image processing. Adv. Intell. Syst. 3, 2000202 (2021).

    Article 

    Google Scholar
     

  • Hanani, M. Satellite glial cells in sensory ganglia: from form to function. Brain Res. Rev. 48, 457–476 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Stein, R. B., Aoyagi, Y., Weber, D. J., Shoham, S. & Normann, R. A. Encoding mechanisms for sensory neurons studied with a multielectrode array in the cat dorsal root ganglion. Can. J. Physiol. Pharmacol. 82, 757–768 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Masland, R. H. The neuronal organization of the retina. Neuron 76, 266–280 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Turner, M. H., Sanchez Giraldo, L. G., Schwartz, O. & Rieke, F. Stimulus- and goal-oriented frameworks for understanding natural vision. Nat. Neurosci. 22, 15–24 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, B., Hong, A., Rieke, F. & Manookin, M. B. Predictive encoding of motion begins in the primate retina. Nat. Neurosci. 24, 1280–1291 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, F. & Chai, Y. Near-sensor and in-sensor computing. Nat. Electron. 3, 664–671 (2020).

    Article 

    Google Scholar
     

  • Fabre, W., Haroun, K., Lorrain, V., Lepecq, M. & Sicard, G. From near-sensor to in-sensor: a state-of-the-art review of embedded AI vision systems. Sensors 24, 5446 (2024).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Modak, N. & Roy, K. Energy efficiency through in-sensor computing: ADC-less real-time sensing for image edge detection. In Proc. 29th ACM/IEEE International Symposium on Low Power Electronics and Design 1–6 (ACM, Newport Beach, CA, USA, 2024).

  • Bae, B. et al. Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space. Sci. Robot. 9, eadl3606 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Tang, W. et al. Review of bio-inspired image sensors for efficient machine vision. Adv. Photonics. 6, 024001 (2024).

  • Liu, J. et al. Recent progress in wearable near-sensor and in-sensor intelligent perception systems. Sensors 24, 2180 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bae, B., Park, M., Lee, D., Sim, I. & Lee, K. Hetero-integrated InGaAs photodiode and oxide memristor-based artificial optical nerve for in-sensor NIR image processing. Adv. Opt. Mater. https://doi.org/10.1002/adom.202201905 (2022).

  • Baek, Y. et al. Network of artificial olfactory receptors for spatiotemporal monitoring of toxic gas. Sci. Adv. 10, eadr2659 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bae, B. et al. Near-sensor computing-assisted simultaneous viral antigen and antibody detection via integrated biosensors with microfluidics. InfoMat 5, e12471 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Pinkham, R., Erhardt, J., De Salvo, B., Berkovich, A. & Zhang, Z. ANSA: adaptive near-sensor architecture for dynamic DNN processing in compact form factors. IEEE Trans. Circuits Syst. Regul. Pap. 70, 1256–1269 (2023).

    Article 

    Google Scholar
     

  • Safa, A., Van Assche, J., Alea, M. D., Catthoor, F. & Gielen, G. G. E. Neuromorphic near-sensor computing: from event-based sensing to edge learning. IEEE Micro 42, 88–95 (2022).

    Article 

    Google Scholar
     

  • Vitale, A., Donati, E., Germann, R. & Magno, M. Neuromorphic edge computing for biomedical applications: gesture classification using EMG signals. IEEE Sens. J. 22, 19490–19499 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Baek, Y. et al. Quantized neural network via synaptic segregation based on ternary charge-trap transistors. Adv. Electron. Mater. 9, 2300303 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Jiang, H. et al. Sub-10 nm Ta channel responsible for superior performance of a HfO2Memristor. Sci. Rep. 6, 1–8 (2016).


    Google Scholar
     

  • Park, M. et al. An artificial neuromuscular junction for enhanced reflexes and oculomotor dynamics based on a ferroelectric CuInP 2 S 6 /GaN HEMT. Sci. Adv. 9, eadh9889 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, J. Y., Choi, M.-J. & Jang, H. W. Ferroelectric field effect transistors: Progress and perspective. APL Mater. 9, 021102 (2021).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Agarwal, S. et al. Using floating gate memory to train ideal accuracy neural networks. IEEE J. Explor. Solid State Comput. Devices Circuits 5, 52–57 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Zhou, Y. et al. Computational event-driven vision sensors for in-sensor spiking neural networks. Nat. Electron. 6, 870–878 (2023).

    Article 

    Google Scholar
     

  • Lin, N. et al. In-memory and in-sensor reservoir computing with memristive devices. APL Mach. Learn. 2, 010901 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Liu, J. et al. TFT-based near-sensor in-memory computing: circuits and architecture perspectives of large-area eDRAM and ROM CiM chips. IEEE Trans. Circuits Syst. Regul. Pap. 71, 620–633 (2024).

    Article 

    Google Scholar
     

  • Diaz-Madrid, J.-A., Domenech-Asensi, G., Ruiz-Merino, R. & Zapata-Perez, J.-F. A real-time and energy-efficient SRAM with mixed-signal in-memory computing near CMOS sensors. J. Real-Time Image Process. 21, 143 (2024).

    Article 

    Google Scholar
     

  • Zhang, Y. et al. Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging. Nat. Commun. 12, 7232 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • He, W. et al. Customized binary and multi-level HfO2−x-based memristors tuned by oxidation conditions. Sci. Rep. 7, 10070 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liang, K.-D. et al. Single CuOx nanowire memristor: forming-free resistive switching behavior. ACS Appl. Mater. Interfaces 6, 16537–16544 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Z. Fan, X. Fan, Li, A. & Dong, L. Resistive switching in copper oxide nanowire-based memristor. In 2012 12th IEEE International Conference on Nanotechnology (IEEE-NANO) 1–4 (IEEE, Birmingham, United Kingdom, 2012).

  • Yang, J. J. et al. Metal/TiO2 interfaces for memristive switches. Appl. Phys. A 102, 785–789 (2011).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Illarionov, G. A., Morozova, S. M., Chrishtop, V. V., Einarsrud, M.-A. & Morozov, M. I. Memristive TiO2: synthesis, technologies, and applications. Front. Chem. 8, 724 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jang, J. et al. A learning-rate modulable and reliable TiOx memristor array for robust, fast, and accurate neuromorphic computing. Adv. Sci. 9, 2201117 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures. Nat. Mater. 10, 625–630 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, Z. et al. Engineering incremental resistive switching in TaO: Xbased memristors for brain-inspired computing. Nanoscale 8, 14015–14022 (2016).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Naqi, M. et al. Multilevel artificial electronic synaptic device of direct grown robust MoS2 based memristor array for in-memory deep neural network. Npj 2D Mater. Appl. 6, 53 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Dev, D. et al. 2D MoS2-based threshold switching memristor for artificial neuron. IEEE Electron Device Lett. 41, 936–939 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Zhang, W. et al. An ultrathin memristor based on a two-dimensional WS2 /MoS2 heterojunction. Nanoscale 13, 11497–11504 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhou, G. et al. Volatile and nonvolatile memristive devices for neuromorphic computing. Adv. Electron. Mater. 8, 2101127 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Jiang, H. et al. A novel true random number generator based on a stochastic diffusive memristor. Nat. Commun. 8, 882 (2017).

  • Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bae, J. et al. Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems. Sci. Adv. 10, eadm7221 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cho, H. et al. Real-time finger motion recognition using skin-conformable electronics. Nat. Electron. 6, 619–629 (2023).

    Article 

    Google Scholar
     

  • Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, X. et al. An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11, 51 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018).

    Article 

    Google Scholar
     

  • Sivan, M. et al. All WSe2 1T1R resistive RAM cell for future monolithic 3D embedded memory integration. Nat. Commun. 10, 5201 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, R. et al. Recent advances of volatile memristors: devices, mechanisms, and applications. Adv. Intell. Syst. 2, 2000055 (2020).

    Article 

    Google Scholar
     

  • Kurt, O., Le, T., Sahu, S. K., Randall, C. A. & Ren, Y. Assessment of strain relaxation and oxygen vacancy migration near grain boundary in SrTiO3 bicrystals by second harmonic generation. J. Phys. Chem. C. 124, 11892–11901 (2020).

  • Xi, J. Strain effects on oxygen vacancy energetics in KTaO3. Phys. Chem. Chem. Phys. https://doi.org/10.1039/c6cp08315c (2017).

  • Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nat. Rev. Mater. 7, 575–591 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Bae, B., Park, M., Lee, D., Sim, I. & Lee, K. Hetero-integrated InGaAs photodiode and oxide memristor-based artificial optical nerve for in-sensor NIR image processing. Adv. Opt. Mater. 11, 2201905 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Du, C. et al. Reservoir computing using dynamic memristors for temporal information processing. Nat. Commun. 8, 1–10 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Park, S.-O., Jeong, H., Park, J., Bae, J. & Choi, S. Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing. Nat. Commun. 13, 2888 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Camuñas-Mesa, L., Linares-Barranco, B. & Serrano-Gotarredona, T. Neuromorphic spiking neural networks and their memristor-CMOS hardware implementations. Materials 12, 2745 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, X., Dang, B., Zhang, T., Wu, X. & Yang, Y. Spatiotemporal audio feature extraction with dynamic memristor-based time-surface neurons. Sci. Adv. 10, eadl2767 (2024).

    Article 
    ADS 
    CAS 
    PubMed Central 

    Google Scholar
     

  • Reis, D., Niemier, M. & Hu, X. S. Computing in memory with FeFETs. In Proceedings of the International Symposium on Low Power Electronics and Design 1–6 (ACM, Seattle WA USA, 2018).

  • Ryu, H. et al. Low-thermal-budget ferroelectric field-effect transistors based on CuInP2 S6 and InZnO. ACS Appl. Mater. Interfaces 15, 53671–53677 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yang, J. Y. et al. Reconfigurable physical reservoir in GaN/α-In2Se3 HEMTs enabled by out-of-plane local polarization of ferroelectric 2D layer. ACS Nano 17, 7695–7704 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yang, J. Y. et al. Pulsed E-/D-mode switchable GaN HEMTs with a ferroelectric AlScN gate dielectric. IEEE Electron Device Lett. 44, 1260–1263 (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Mondal, S. et al. ScAlN-based ITO channel ferroelectric field-effect transistors with large memory window. IEEE Trans. Electron Devices 70, 4618–4621 (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Li, Q. et al. High-performance ferroelectric field-effect transistors with ultra-thin indium tin oxide channels for flexible and transparent electronics. Nat. Commun. 15, 2686 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, J. Y. et al. Reconfigurable radio-frequency high-electron mobility transistors via ferroelectric-based gallium nitride heterostructure. Adv. Electron. Mater. 8, 2101406 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Xiao, W. et al. Memory window and endurance improvement of Hf0.5Zr0.5O2-based FeFETs with ZrO2 seed layers characterized by fast voltage pulse measurements. Nanoscale Res. Lett. 14, 254 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Milloch, A., Fabrizio, M. & Giannetti, C. Mott materials: unsuccessful metals with a bright future. Npj Spintron. 2, 49 (2024).

    Article 

    Google Scholar
     

  • Shukla, N. et al. A steep-slope transistor based on abrupt electronic phase transition. Nat. Commun. 6, 7812 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parihar, A., Shukla, N., Jerry, M., Datta, S. & Raychowdhury, A. Computing with dynamical systems based on insulator-metal-transition oscillators. Nanophotonics 6, 601–611 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Wang, X. et al. Multimechanism synergistic photodetectors with ultrabroad spectrum response from 375 nm to 10 µm. Adv. Sci. 6, 1901050 (2019).

    Article 

    Google Scholar
     

  • Wu, G. et al. Visible to short wavelength infrared In2 Se3-nanoflake photodetector gated by a ferroelectric polymer. Nanotechnology 27, 364002 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Chen, Y. et al. Optoelectronic properties of few-layer MoS2 FET gated by ferroelectric relaxor polymer. ACS Appl. Mater. Interfaces 8, 32083–32088 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liao, C.-S., Ding, Y.-F., Zhao, Y.-Q. & Cai, M.-Q. Band alignment engineering of a Ruddlesden–Popper perovskite-based heterostructure constructed using Cs2SnI2Cl2 and α-In2Se3: The effects of ferroelectric polarization switching and electric fields. Appl. Phys. Lett. 119, 182903 (2021).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Gkoupidenis, P. et al. Organic mixed conductors for bioinspired electronics. Nat. Rev. Mater. 9, 134–149 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Rivnay, J. et al. Organic electrochemical transistors. Nat. Rev. Mater. 3, 17086 (2018).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Gkoupidenis, P., Schaefer, N., Strakosas, X., Fairfield, J. A. & Malliaras, G. G. Synaptic plasticity functions in an organic electrochemical transistor. Appl. Phys. Lett. 107, 263302 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Kim, W. et al. Electrochemiluminescent tactile visual synapse enabling in situ health monitoring. Nat. Mater. https://doi.org/10.1038/s41563-025-02124-x (2025).

  • Lee, Y. R., Trung, T. Q., Hwang, B.-U. & Lee, N.-E. A flexible artificial intrinsic-synaptic tactile sensory organ. Nat. Commun. 11, 2753 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, K. et al. Artificially intelligent tactile ferroelectric skin. Adv. Sci. 7, 2001662 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Wu, X. et al. Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning. Nat. Commun. 14, 468 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yin, Y. et al. In-sensor organic electrochemical transistor for the multimode neuromorphic olfactory system. ACS Sens. 9, 4277–4285 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Paulsen, B. D., Tybrandt, K., Stavrinidou, E. & Rivnay, J. Organic mixed ionic–electronic conductors. Nat. Mater. 19, 13–26 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Keene, S. T. et al. Hole-limited electrochemical doping in conjugated polymers. Nat. Mater. 22, 1121–1127 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moro, S. et al. The effect of glycol side chains on the assembly and microstructure of conjugated polymers. ACS Nano 16, 21303–21314 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, D. et al. A wearable in-sensor computing platform based on stretchable organic electrochemical transistors. Nat. Electron. 7, 1176–1185 (2024).

    Article 

    Google Scholar
     

  • Wang, S. et al. An organic electrochemical transistor for multi-modal sensing, memory and processing. Nat. Electron. 6, 281–291 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Chouhdry, H. H., Lee, D. H., Bag, A. & Lee, N.-E. A flexible artificial chemosensory neuronal synapse based on chemoreceptive ionogel-gated electrochemical transistor. Nat. Commun. 14, 821 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, B. et al. ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing. Nanoscale Horiz. 6, 939–970 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Nasiri, N., Jin, D. & Tricoli, A. Nanoarchitechtonics of visible-blind ultraviolet photodetector materials: critical features and nano-microfabrication. Adv. Opt. Mater. 7, 1800580 (2019).

    Article 

    Google Scholar
     

  • Li, G. et al. Interface-engineered non-volatile visible-blind photodetector for in-sensor computing. Nat. Commun. 16, 57 (2025).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cui, B. et al. Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision. Nat. Commun. 13, 1707 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vasilopoulou, M. et al. Neuromorphic computing based on halide perovskites. Nat. Electron. 6, 949–962 (2023).

    Article 
    CAS 

    Google Scholar
     

  • He, Z. et al. Perovskite retinomorphic image sensor for embodied intelligent vision. Sci. Adv. 11, eads2834 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, M.-Z. et al. Inorganic perovskite quantum dot-based strain sensors for data storage and in-sensor computing. ACS Appl. Mater. Interfaces 13, 30861–30873 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sharma, D. et al. Halide perovskite photovoltaics for in-sensor reservoir computing. Nano Energy 129, 109949 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Zhou, X. et al. All-photonic artificial synapses based on photochromic perovskites for noncontact neuromorphic visual perception. Commun. Mater. 5, 116 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Chen, Q. et al. Switchable perovskite photovoltaic sensors for bioinspired adaptive machine vision. Adv. Intell. Syst. 2, 2000122 (2020).

    Article 

    Google Scholar
     

  • Shao, H. et al. A reconfigurable optoelectronic synaptic transistor with stable Zr-CsPbI3 nanocrystals for visuomorphic computing. Adv. Mater. 35, 2208497 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Zhang, X. et al. Halide perovskite memristors for optoelectronic memory and computing applications. Inf. Funct. Mater. 1, 265–281 (2024).


    Google Scholar
     

  • Liu, C. et al. Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15, 545–557 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Geim, A. K. & Grigorieva, I. V. Van der Waals heterostructures. Nature 499, 419–425 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, Y. et al. MXene-ZnO memristor for multimodal in-sensor computing. Adv. Funct. Mater. 31, 2100144 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Ghosh, S. et al. An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior. Nat. Commun. 14, 6021 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shi, Y., Duong, N. T. & Ang, K.-W. Emerging 2D materials hardware for in-sensor computing. Nanoscale Horiz. 10, 205–229 (2025).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Qi, M. et al. An in-sensor humidity computing system for contactless human–computer interaction. Mater. Horiz. 11, 939–948 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sun, L. et al. In-sensor reservoir computing for language learning via two-dimensional memristors. Sci. Adv. 7, eabg1455 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, Y., Li, D., Wu, C.-L., Hwang, H. Y. & Cui, Y. Electrostatic gating and intercalation in 2D materials. Nat. Rev. Mater. 8, 41–53 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Jayachandran, D. et al. A low-power biomimetic collision detector based on an in-memory molybdenum disulfide photodetector. Nat. Electron. 3, 646–655 (2020).

    Article 

    Google Scholar
     

  • Wu, G. et al. Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing. Nat. Mater. 22, 1499–1506 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Das, B. et al. Artificial visual systems fabricated with ferroelectric van der Waals heterostructure for in-memory computing applications. ACS Nano 17, 21297–21306 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Moon, D. et al. Hypotaxy of wafer-scale single-crystal transition metal dichalcogenides. Nature 638, 957–964 (2025).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Chu, Q.-Q. et al. Encapsulation: the path to commercialization of stable perovskite solar cells. Matter 6, 3838–3863 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Wu, E. et al. A CMOS-compatible fabrication approach for high-performance perovskite photodetector arrays. Adv. Opt. Mater. 13, 2402979 (2025).

    Article 
    CAS 

    Google Scholar
     

  • Duff, I. S. & Stewart, G. W. Sparse Matrix Proceedings, 1978 (SIAM, 1979).

  • Chen, T. et al. DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning. SIGARCH Comput Arch. News 42, 269–284 (2014).

    Article 

    Google Scholar
     

  • Chen, Y.-H., Emer, J. & Sze, V. Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. SIGARCH Comput Arch. News 44, 367–379 (2016).

    Article 

    Google Scholar
     

  • Ali, M. et al. IMAC: In-Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array. IEEE Trans. Circuits Syst. Regul. Pap. 67, 2521–2531 (2020).

    Article 

    Google Scholar
     

  • Zhang, J., Wang, Z. & Verma, N. In-memory computation of a machine-learning classifier in a standard 6T SRAM array. IEEE J. Solid State Circuits 52, 915–924 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Fujiwara, H. et al. 34.4 A 3nm, 32.5TOPS/W, 55.0TOPS/mm2 and 3.78Mb/mm2 fully-digital compute-in-memory macro supporting INT12 × INT12 with a parallel-MAC architecture and foundry 6T-SRAM Bit Cell. In 2024 IEEE International Solid-State Circuits Conference (ISSCC) vol. 67 572–574 (2024).

  • Yin, G. et al. Enabling lower-power charge-domain nonvolatile in-memory computing with ferroelectric FETs. IEEE Trans. Circuits Syst. II Express Briefs 68, 2262–2266 (IEEE, 2021).

  • Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bayat, F. M. et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat. Commun. 9, 2331 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xue, C.-X. et al. 15.4 A 22nm 2Mb ReRAM compute-in-memory macro with 121-28TOPS/W for multibit MAC computing for tiny AI edge devices. In 2020 IEEE International Solid-State Circuits Conference – (ISSCC) 244–246 https://doi.org/10.1109/ISSCC19947.2020.9063078 (2020).

  • Jin, C. et al. A multi-bit CAM design with ultra-high density and energy efficiency based on FeFET NAND. IEEE Electron Device Lett. 44, 1104–1107 (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Jaiswal, A., Roy, S., Srinivasan, G. & Roy, K. Proposal for a leaky-integrate-fire spiking neuron based on magnetoelectric switching of ferromagnets. IEEE Trans. Electron Devices 64, 1818–1824 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Dutta, S., Kumar, V., Shukla, A., Mohapatra, N. R. & Ganguly, U. Leaky integrate and fire neuron by charge-discharge dynamics in floating-body MOSFET. Sci. Rep. 7, 8257 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Krestinskaya, O., James, A. P. & Chua, L. O. Neuromemristive circuits for edge computing: a review. IEEE Trans. Neural Netw. Learn. Syst. 31, 4–23 (2020).

    Article 
    MathSciNet 
    PubMed 

    Google Scholar
     

  • Sonnadara, C. & Shah, S. Real-time analog processing with on-chip learning using multiple-input translinear elements. npj Unconv Comp. 2, 11 (2025).

  • Sonnadara, C. & Shah, S. On-chip adaptation for reducing mismatch in analog non-volatile device based neural networks. In 2024 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5. https://doi.org/10.1109/ISCAS58744.2024.10557839 (2024).

  • Gokmen, T. Enabling training of neural networks on noisy hardware. Front. Artif. Intell. 4, 699148 (2021).

  • Long, Y. et al. A ferroelectric FET-based processing-in-memory architecture for DNN acceleration. IEEE J. Explor. Solid State Comput. Devices Circuits 5, 113–122 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Park, M. et al. Remote epitaxy and freestanding wide bandgap semiconductor membrane technology. Nat. Rev. Electr. Eng. 1, 680–689 (2024).

    Article 

    Google Scholar
     

  • Liu, Y., Fan, R., Guo, J., Ni, H. & Bhutta, M. U. M. In-sensor visual perception and inference. Intell. Comput. 2, 0043 (2023).

    Article 

    Google Scholar
     

  • Lee, S., Peng, R., Wu, C. & Li, M. Programmable black phosphorus image sensor for broadband optoelectronic edge computing. Nat. Commun. 13, 1485 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shao, H. et al. Adaptive in-sensor computing for enhanced feature perception and broadband image restoration. Adv. Mater. 37, 2414261 (2025).

    Article 
    CAS 

    Google Scholar
     

  • Nair, G. R. et al. 3-D in-sensor computing for real-time DVS data compression: 65-nm hardware-algorithm co-design. IEEE Solid State Circuits Lett. 7, 119–122 (2024).

    Article 

    Google Scholar
     

  • Yuan, S. et al. Geometric deep optical sensing. Science 379, eade1220 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Cao, Z. et al. A programmable electronic skin with event-driven in-sensor touch differential and decision-making. Adv. Funct. Mater. 35, 2412649 (2025).

    Article 
    CAS 

    Google Scholar
     

  • Li, K. et al. Thin-film event-based vision sensors for enhanced multispectral perception beyond human vision. InfoMat https://doi.org/10.1002/inf2.70007 (2025).

  • Candes, E. J., Romberg, J. & Tao, T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489–509 (2006).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Olshausen, B. A. & Field, D. J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 14, 481–487 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Martinez, J. A., Ruiz, P. M. & Skarmeta, A. F. Evaluation of the use of compressed sensing in data harvesting for vehicular sensor networks. Sensors 20, 1434 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xue, Y., Lau, V. & Cai, S. Efficient sparse coding using hierarchical Riemannian pursuit. IEEE Trans. Signal Process. 69, 4069–4084 (2021).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Jacob, B. et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2704–2713 (IEEE, Salt Lake City, UT, 2018).

  • Gholami, A. et al. A survey of quantization methods for efficient neural network inference. In Low-Power Computer Vision (Chapman and Hall/CRC, 2022).

  • Nagel, M. et al. A white paper on neural network quantization. Preprint at https://doi.org/10.48550/arXiv.2106.08295 (2021).

  • McMahan, B., Moore, E., Ramage, D., Hampson, S. & Arcas, B. A. y. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 1273–1282 (PMLR, 2017).

  • Shen, C., Yang, J. & Xu, J. On federated learning with energy harvesting clients. In ICASSP 2022 – 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 8657–8661 (2022).

  • Mu, Y. & Shen, C. Communication and storage efficient federated split learning. In ICC 2023 – IEEE International Conference on Communications 2976–2981 https://doi.org/10.1109/ICC45041.2023.10278891 (2023).

  • Wang, J. et al. A 28-nm compute SRAM with bit-serial logic/arithmetic operations for programmable in-memory vector computing. IEEE J. Solid State Circuits 55, 76–86 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Song, W. et al. Programming memristor arrays with arbitrarily high precision for analog computing. Science 383, 903–910 (2024).

    Article 
    ADS 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, J. et al. Drift-aware feature learning based on autoencoder preprocessing for soft sensors. Adv. Intell. Syst 6, 2300486 (2024).

    Article 

    Google Scholar
     

  • Eldebiky, A., Zhang, G. L., Boecherer, G., Li, B. & Schlichtmann, U. CorrectNet: robustness enhancement of analog in-memory computing for neural networks by error suppression and compensation. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1–6 (2023).

  • Xiao, Z. et al. Multimodal in-sensor computing system using integrated silicon photonic convolutional processor. Adv. Sci. 11, 2408597 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Jiang, C. et al. 60 nm Pixel-size pressure piezo-memory system as ultrahigh-resolution neuromorphic tactile sensor for in-chip computing. Nano Energy 87, 106190 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Chun, S. et al. An artificial neural tactile sensing system. Nat. Electron. https://doi.org/10.1038/s41928-021-00585-x (2021).

  • Otseidu, K., Jia, T., Bryne, J., Hargrove, L. & Gu, J. Design and optimization of edge computing distributed neural processor for biomedical rehabilitation with sensor fusion. In Proc. International Conference on Computer-Aided Design 1–8 (ACM, San Diego, CA, 2018).

  • Liu, X. et al. Near-sensor reservoir computing for gait recognition via a multi-gate electrolyte-gated transistor. Adv. Sci. 10, 2300471 (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Yang, H. et al. Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction. Nat. Commun. 13, 5311 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ma, S. et al. Bioinspired in-sensor multimodal fusion for enhanced spatial and spatiotemporal association. Nano Lett. 24, 7091–7099 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Liang, X. et al. Rotating neurons for all-analog implementation of cyclic reservoir computing. Nat. Commun. 13, 1549 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Z. et al. In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array. Nat. Commun. 13, 6590 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, S. et al. Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot. Nat. Commun. 15, 7056 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, C. et al. Bioinspired artificial sensory nerve based on nafion memristor. Adv. Funct. Mater. 29, 1808783 (2019).

    Article 

    Google Scholar
     

  • Rehman, S., Khan, M. F., Kim, H.-D. & Kim, S. Analog–digital hybrid computing with SnS2 memtransistor for low-powered sensor fusion. Nat. Commun. 13, 2804 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moosmann, J. et al. Ultra-efficient on-device object detection on AI-integrated smart glasses with TinyissimoYOLO. European Conference on Computer Vision. Cham: Springer Nature Switzerland, 262–280 (2024).

  • Lee, S.-W. et al. An artificial olfactory sensory neuron for selective gas detection with in-sensor computing. Device 1, 100063 (2023).

    Article 

    Google Scholar
     

  • Jang, H. et al. In-sensor optoelectronic computing using electrostatically doped silicon. Nat. Electron. 5, 519–525 (2022).

    Article 

    Google Scholar
     

  • Kapoor, R., Anastasiu, D. C. & Choi, S. ML-NIC: accelerating machine learning inference using smart network interface cards. Front. Comput. Sci. 6, 1493399 (2025).

    Article 

    Google Scholar
     

  • Du, Y. et al. Monolithic 3D integration of analog RRAM-based computing-in-memory and sensor for energy-efficient near-sensor computing. Adv. Mater. 36, 2302658 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Valenzuela, W., Saavedra, A., Zarkesh-Ha, P. & Figueroa, M. Motion-based object location on a smart image sensor using on-pixel memory. Sensors 22, 6538 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ma, S. et al. BitNet b1.58 2B4T technical report. Preprint at https://doi.org/10.48550/arXiv.2504.12285 (2025).

  • Kandala, S. V., Medaranga, P. & Varshney, A. TinyLLM: A framework for training and deploying language models at the edge computers. Preprint at https://doi.org/10.48550/arXiv.2412.15304 (2024).

  • Shen, X. et al. HotaQ: hardware oriented token adaptive quantization for large language models. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. https://doi.org/10.1109/TCAD.2024.3487781 (2024).

  • Zheng, Y. et al. A review on edge large language models: design, execution, and applications. ACM Comput Surv 57, 209:1–209:35 (2025).

    Article 

    Google Scholar
     

  • Cai, F., Yuan, D., Yang, Z. & Cui, L. Edge-LLM: A collaborative framework for large language model serving in edge computing. In 2024 IEEE International Conference on Web Services (ICWS) 799–809 https://doi.org/10.1109/ICWS62655.2024.00099 (2024).