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Neuromorphic olfactory perception chips: towards universal odour recognition and cognition

Nature Reviews Electrical Engineering volume 2, pages 755–772 (2025)Cite this article

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Abstract

Olfactory perception, one of the most complex and enigmatic senses, has a crucial role in various aspects of human life. However, mimicking the extraordinary capabilities of the biological olfactory system in electronic devices remains a formidable challenge. Neuromorphic olfactory perception chips, inspired by the intricate architecture and functions of the olfactory pathway, have emerged as a promising solution. By integrating microelectronics and nanoelectronics with artificial intelligence technologies, these chips aim to replicate the ability of the human olfactory system to discriminate and recognize a vast array of odours with high sensitivity, specificity and low-power consumption. The unique features of olfactory perception, such as high-dimensional odour space and complex spatiotemporal coding, pose distinct hindrances for these chips. Researchers are leveraging memristors and spiking neural networks to enable real-time odour perception, learning and recognition. Integrating sensing, computing and memory within these chips represents a substantial leap towards efficient olfactory information processing. This interdisciplinary innovation is revolutionizing applications in environmental monitoring, food quality control, medical diagnosis and emotional communication. Developing neuromorphic olfactory chips is critical for overcoming the limitation of traditional gas sensors and for elevating olfactory machine intelligence. As research advances, neuromorphic olfactory perception chips are poised to unlock new frontiers in understanding and emulating the human sense of smell.

Key points

  • Neuromorphic olfactory chips mimic biological olfactory systems, integrating micro–nanoelectronics and artificial intelligence to achieve high-sensitivity, low-power odour recognition through combinatorial coding and distributed neural representation.

  • Memristors and spiking neural networks enable real-time odour processing, leveraging synaptic plasticity and sparse coding to enhance efficiency and adaptability.

  • Sense–memory–compute integration reduces energy consumption by combining odour sensing, in-memory computation and adaptive learning on-chip, mimicking biological neural pathways.

  • Potential uses for neuromorphic olfactory chips include environmental monitoring, medical diagnostics, food safety and robotics, offering portable, non-invasive solutions for odour detection.

  • Material stability, sensor batch production, algorithm robustness and cross-modal integration hinder scalability, requiring advances in synthetic biology and hybrid sensing systems.

  • Future directions include the development of biohybrid materials, multiscale heterogeneous chip architectures and brain-inspired algorithms to achieve human-like olfactory universality and energy efficiency.

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Fig. 1: Neuromorphic olfactory systems inspired by biological olfaction.
Fig. 2: Extracting and processing high-dimensional odour features.
Fig. 3: Neuromorphic computing leveraging synaptic plasticity for bio-inspired intelligent olfaction.
Fig. 4: Diverse hardware architectures incorporating mixed-signal design for emulating neural computation.
Fig. 5: Systolic array architectures and modular digital systems for efficient neural computation.

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References

  1. Bushdid, C., Magnasco, M. O., Vosshall, L. B. & Keller, A. Humans can discriminate more than 1 trillion olfactory stimuli. Science 343, 1370–1372 (2014).

    Article  Google Scholar 

  2. Liu, T. et al. Review on algorithm design in electronic noses: challenges, status, and trends. Intell. Comput. 2, 0012 (2023).

    Article  Google Scholar 

  3. Ham, D., Park, H., Hwang, S. & Kim, K. Neuromorphic electronics based on copying and pasting the brain. Nat. Electron. 4, 635–644 (2021).

    Article  Google Scholar 

  4. Zhou, Z., Xu, T. & Zhang, X. Empowerment of AI algorithms in biochemical sensors. Trends Anal. Chem. 173, 117613 (2024).

    Article  Google Scholar 

  5. Donati, E. & Valle, G. Neuromorphic hardware for somatosensory neuroprostheses. Nat. Commun. 15, 556 (2024).

    Article  Google Scholar 

  6. Son, M., Lee, J. Y., Ko, H. J. & Park, T. H. Bioelectronic nose: an emerging tool for odor standardization. Trends Biotechnol. 35, 301–307 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. 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  Google Scholar 

  9. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529–544 (2020).

    Article  Google Scholar 

  10. Chun, S. Y. et al. An artificial olfactory system based on a chemi-memristive device. Adv. Mater. 35, 2302219 (2023).

    Article  Google Scholar 

  11. Lee, D., Chae, M., Jung, J. & Kim, H.-D. Correlation between sensing accuracy and read margin of a memristor-based NO gas sensor array estimated by neural network analysis. ACS Sens. 8, 2105–2114 (2023).

    Article  Google Scholar 

  12. Wang, C. et al. Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor arrays. Nat. Electron. 7, 157–167 (2024).

    Article  Google Scholar 

  13. Schuman, C. D. et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2, 10–19 (2022).

    Article  Google Scholar 

  14. Kimovski, D. et al. Beyond Von Neumann in the computing continuum: architectures, applications, and future directions. IEEE Internet Comput. 28, 6–16 (2024).

    Article  Google Scholar 

  15. Wan, T. et al. In-sensor computing: materials, devices, and integration technologies. Adv. Mater. 35, 2203830 (2023).

    Article  Google Scholar 

  16. Xia, Q. & Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019).

    Article  Google Scholar 

  17. Huang, Y. et al. Memristor-based hardware accelerators for artificial intelligence. Nat. Rev. Electr. Eng. 1, 286–299 (2024).

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Reda, S. 3D integration advances computing. Nature 547, 38–39 (2017).

    Article  Google Scholar 

  20. Shulaker, M. M. et al. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature 547, 74–78 (2017).

    Article  Google Scholar 

  21. Hills, G. et al. Modern microprocessor built from complementary carbon nanotube transistors. Nature 572, 595–602 (2019).

    Article  Google Scholar 

  22. Franklin, A. D., Hersam, M. C. & Wong, H. S. P. Carbon nanotube transistors: making electronics from molecules. Science 378, 726–732 (2022).

    Article  Google Scholar 

  23. Buck, L. & Axel, R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991).

    Article  Google Scholar 

  24. Mori, K., Nagao, H. & Yoshihara, Y. The olfactory bulb: coding and processing of odor molecule information. Science 286, 711–715 (1999).

    Article  Google Scholar 

  25. Ache, B. W. & Young, J. M. Olfaction: diverse species, conserved principles. Neuron 48, 417–430 (2005).

    Article  Google Scholar 

  26. Lewcock, J. W. & Reed, R. R. ORs rule the roost in the olfactory system. Science 302, 2078–2079 (2003).

    Article  Google Scholar 

  27. Zou, Z. & Buck, L. B. Combinatorial effects of odorant mixes in olfactory cortex. Science 311, 1477–1481 (2006).

    Article  Google Scholar 

  28. Aungst, J. L. et al. Centre–surround inhibition among olfactory bulb glomeruli. Nature 426, 623–629 (2003).

    Article  Google Scholar 

  29. Fang, C. et al. Smart electronic nose enabled by an all-feature olfactory algorithm. Adv. Intell. Syst. 4, 2200074 (2022).

    Article  Google Scholar 

  30. Simon, U., Sanders, D., Jockel, J. & Brinz, T. Setup for high-throughput impedance screening of gas-sensing materials. J. Comb. Chem. 7, 682–687 (2005).

    Article  Google Scholar 

  31. Siemons, M., Koplin, T. J. & Simon, U. Advances in high throughput screening of gas sensing materials. Appl. Surf. Sci. 254, 669–676 (2007).

    Article  Google Scholar 

  32. Imam, N. & Cleland, T. A. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat. Mach. Intell. 2, 181–191 (2020).

    Article  Google Scholar 

  33. Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

    Article  Google Scholar 

  34. Najmaei, S. et al. Advancements in materials, devices, and integration schemes for a new generation of neuromorphic computers. Mater. Today 59, 80–106 (2022).

    Article  Google Scholar 

  35. Marchisio, A. & Shafique, M. in Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Software Optimizations and Hardware/Software Codesign (eds Pasricha, S. & Shafique, M.) 137–172 (Springer Nature Switzerland, 2024).

  36. Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019).

    Article  Google Scholar 

  37. Olsen, S. R. & Wilson, R. I. Lateral presynaptic inhibition mediates gain control in an olfactory circuit. Nature 452, 956–960 (2008).

    Article  Google Scholar 

  38. Han, J.-K. et al. Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware. Sci. Adv. 7, eabg8836 (2021).

    Article  Google Scholar 

  39. Kim, S. et al. Pattern recognition using carbon nanotube synaptic transistors with an adjustable weight update protocol. ACS Nano 11, 2814–2822 (2017).

    Article  Google Scholar 

  40. Ackels, T. et al. Fast odour dynamics are encoded in the olfactory system and guide behaviour. Nature 593, 558–563 (2021).

    Article  Google Scholar 

  41. Bathellier, B., Buhl, D. L., Accolla, R. & Carleton, A. Dynamic ensemble odor coding in the mammalian olfactory bulb: sensory information at different timescales. Neuron 57, 586–598 (2008).

    Article  Google Scholar 

  42. Guo, M., Brewster, J. T. II, Zhang, H., Zhao, Y. & Zhao, Y. Challenges and opportunities of chemiresistors based on microelectromechanical systems for chemical olfaction. ACS Nano 16, 17778–17801 (2022).

    Article  Google Scholar 

  43. Mei et al. Overcoming the limits of cross-sensitivity: pattern recognition methods for chemiresistive gas sensor array. Nano–Micro Lett. 16, 269 (2024).

    Article  Google Scholar 

  44. Meena, D., Jain, M. & Bhatnagar, M. C. Resistive gas sensors based on nanostructured ternary metal oxide: a review. J. Mater. Sci. 59, 12177–12218 (2024).

    Article  Google Scholar 

  45. Park, S. Y. et al. Chemoresistive materials for electronic nose: progress, perspectives, and challenges. InfoMat 1, 289–316 (2019).

    Article  Google Scholar 

  46. Liu, W. et al. A highly sensitive and moisture-resistant gas sensor for diabetes diagnosis with Pt@In2O3 nanowires and a molecular sieve for protection. NPG Asia Mater. 10, 293–308 (2018).

    Article  Google Scholar 

  47. Khadka, R., Carraher, C., Hamiaux, C., Travas-Sejdic, J. & Kralicek, A. Synergistic improvement in the performance of insect odorant receptor based biosensors in the presence of Orco. Biosens. Bioelectron. 153, 112040 (2020).

    Article  Google Scholar 

  48. Yamada, T. et al. Highly sensitive VOC detectors using insect olfactory receptors reconstituted into lipid bilayers. Sci. Adv. 7, eabd2013 (2021).

    Article  Google Scholar 

  49. Song, H. W. et al. A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices. Sci. Adv. 10, eadl2882 (2024).

    Article  Google Scholar 

  50. Cheema, J. A. et al. Insect odorant receptor nanodiscs for sensitive and specific electrochemical detection of odorant compounds. Sens. Actuators B Chem. 329, 129243 (2021).

    Article  Google Scholar 

  51. Manai, R. et al. Diamond micro-cantilevers as transducers for olfactory receptors-based biosensors: application to the receptors M71 and OR7D4. Sens. Actuators B Chem. 238, 1199–1206 (2017).

    Article  Google Scholar 

  52. Scorsone, E. et al. Biosensor array based on ligand binding proteins for narcotics and explosives detection. Sens. Actuators B Chem. 334, 129587 (2021).

    Article  Google Scholar 

  53. Nardiello, M. et al. Odorant binding proteins from Hermetia illucens: potential sensing elements for detecting volatile aldehydes involved in early stages of organic decomposition. Nanotechnology 33, 205501 (2022).

    Article  Google Scholar 

  54. Lee, K. et al. Highly selective reduced graphene oxide (rGO) sensor based on a peptide aptamer receptor for detecting explosives. Sci. Rep. 9, 10297 (2019).

    Article  Google Scholar 

  55. Wang, Z. et al. High-performance olfactory receptor-derived peptide sensor for trimethylamine detection based on Steglich esterification reaction and native chemical ligation connection. Biosens. Bioelectron. 195, 113673 (2022).

    Article  Google Scholar 

  56. Kim, K. H. et al. Wireless portable bioelectronic nose device for multiplex monitoring toward food freshness/spoilage. Biosens. Bioelectron. 215, 114551 (2022).

    Article  Google Scholar 

  57. Panigrahi, S., Sankaran, S., Mallik, S., Gaddam, B. & Hanson, A. A. Olfactory receptor-based polypeptide sensor for acetic acid VOC detection. Mater. Sci. Eng. C 32, 1307–1313 (2012).

    Article  Google Scholar 

  58. Son, M. et al. Bioelectronic nose using odorant binding protein-derived peptide and carbon nanotube field-effect transistor for the assessment of salmonella contamination in food. Anal. Chem. 88, 11283–11287 (2016).

    Article  Google Scholar 

  59. Ju, S. et al. Single-carbon discrimination by selected peptides for individual detection of volatile organic compounds. Sci. Rep. 5, 9196 (2015).

    Article  Google Scholar 

  60. Sankaran, S., Panigrahi, S. & Mallik, S. Olfactory receptor based piezoelectric biosensors for detection of alcohols related to food safety applications. Sens. Actuators B Chem. 155, 8–18 (2011).

    Article  Google Scholar 

  61. Komikawa, T. et al. A bioinspired peptide matrix for the detection of 2,4,6-trinitrotoluene (TNT). Biosens. Bioelectron. 153, 112030 (2020).

    Article  Google Scholar 

  62. Yang, H. et al. Nanodisc-based bioelectronic nose using olfactory receptor produced in Escherichia coli for the assessment of the death-associated odor cadaverine. ACS Nano 11, 11847–11855 (2017).

    Article  Google Scholar 

  63. Chen, F. et al. Functional expression of olfactory receptors using cell-free expression system for biomimetic sensors towards odorant detection. Biosens. Bioelectron. 130, 382–388 (2019).

    Article  Google Scholar 

  64. Lee, S. H. et al. Mimicking the human smell sensing mechanism with an artificial nose platform. Biomaterials 33, 1722–1729 (2012).

    Article  Google Scholar 

  65. Gao, A. et al. Highly sensitive and selective detection of human-derived volatile organic compounds based on odorant binding proteins functionalized silicon nanowire array. Sens. Actuators B Chem. 309, 127762 (2020).

    Article  Google Scholar 

  66. Kotlowski, C. et al. Fine discrimination of volatile compounds by graphene-immobilized odorant-binding proteins. Sens. Actuators B Chem. 256, 564–572 (2018).

    Article  Google Scholar 

  67. Choi, D. et al. Bioelectrical nose platform using odorant-binding protein as a molecular transporter mimicking human mucosa for direct gas sensing. ACS Sens. 7, 3399–3408 (2022).

    Article  Google Scholar 

  68. Goldsmith, B. R. et al. Biomimetic chemical sensors using nanoelectronic readout of olfactory receptor proteins. ACS Nano 5, 5408–5416 (2011).

    Article  Google Scholar 

  69. Kwon, O. S. et al. An ultrasensitive, selective, multiplexed superbioelectronic nose that mimics the human sense of smell. Nano Lett. 15, 6559–6567 (2015).

    Article  Google Scholar 

  70. Liu, M. et al. Neuronal network-based biomimetic chip for long-term detection of olfactory dysfunction model in early-stage Alzheimer’s disease. Biosens. Bioelectron. 216, 114619 (2022).

    Article  Google Scholar 

  71. Huang, W. et al. Memristive artificial synapses for neuromorphic computing. Nano–Micro Lett. 13, 85 (2021).

    Article  Google Scholar 

  72. Lee, Y., Park, H.-L., Kim, Y. & Lee, T.-W. Organic electronic synapses with low energy consumption. Joule 5, 794–810 (2021).

    Article  Google Scholar 

  73. Christensen, D. V. et al. 2022 roadmap on neuromorphic computing and engineering. Neuromorph. Comput. Eng. 2, 022501 (2022).

    Article  Google Scholar 

  74. Koickal, T. J. et al. Analog VLSI circuit implementation of an adaptive neuromorphic olfaction chip. IEEE Trans. Circuits Syst. I Regul. Pap. 54, 60–73 (2007).

    Article  Google Scholar 

  75. Ng, K. T., Boussaid, F. & Bermak, A. A CMOS single-chip gas recognition circuit for metal oxide gas sensor arrays. IEEE Trans. Circuits Syst. I Regul. Pap. 58, 1569–1580 (2011).

    Article  MathSciNet  Google Scholar 

  76. Huang, P. C. & Rabaey, J. M. A bio-inspired analog gas sensing front end. IEEE Trans. Circuits Syst. I Regul. Pap. 64, 2611–2623 (2017).

    Article  Google Scholar 

  77. Yuan, R. et al. A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human–machine interface. Nat. Commun. 14, 3695 (2023).

    Article  Google Scholar 

  78. Jiménez-Fernández, A. et al. A binaural neuromorphic auditory sensor for FPGA: a spike signal processing approach. IEEE Trans. Neural Netw. Learn. Syst. 28, 804–818 (2017).

    Article  Google Scholar 

  79. Le, T. K., Bui, T. T. & Le, D. H. Modeling and designing of an all-digital resonate-and-fire neuron circuit. IEEE Access. 11, 62318–62336 (2023).

    Article  Google Scholar 

  80. Molin, J. L. & Etienne-Cummings, R. in Handbook of Neuroengineering (ed. Thakor, N. V.) 1–26 (Springer Singapore, 2020).

  81. Han, J.-K. et al. Artificial olfactory neuron for an in-sensor neuromorphic nose. Adv. Sci. 9, 2106017 (2022).

    Article  Google Scholar 

  82. Zampetti, E. et al. Use of a multiplexed oscillator in a miniaturized electronic nose based on a multichannel quartz crystal microbalance. Sens. Actuators B Chem. 131, 159–166 (2008).

    Article  Google Scholar 

  83. Son, J. & Mishra, A. K. A survey of brain inspired technologies for engineering. In Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference 1–6 (IEEE, 2016).

  84. Merolla, P. et al. A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm. In Custom Integrated Circuits Conference 1–4 (IEEE, 2011).

  85. Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    Article  Google Scholar 

  86. Sawada, J. et al. TrueNorth ecosystem for brain-inspired computing: Scalable systems, software, and applications. In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis 130–141 (IEEE, 2016).

  87. Byun, K. et al. Recent advances in synaptic nonvolatile memory devices and compensating architectural and algorithmic methods toward fully integrated neuromorphic chips. Adv. Mater. Technol. 8, 2200884 (2023).

    Article  Google Scholar 

  88. Wang, Z. et al. Reinforcement learning with analogue memristor arrays. Nat. Electron. 2, 115–124 (2019).

    Article  Google Scholar 

  89. Li, C. et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nat. Commun. 9, 2385 (2018).

    Article  Google Scholar 

  90. Choi, S. et al. 3D-integrated multilayered physical reservoir array for learning and forecasting time-series information. Nat. Commun. 15, 2044 (2024).

    Article  Google Scholar 

  91. Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).

    Article  Google Scholar 

  92. Wang, Z. et al. Capacitive neural network with neuro-transistors. Nat. Commun. 9, 3208 (2018).

    Article  Google Scholar 

  93. Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).

    Article  Google Scholar 

  94. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

  96. Kim, H., Mahmoodi, M. R., Nili, H. & Strukov, D. B. 4K-memristor analog-grade passive crossbar circuit. Nat. Commun. 12, 5198 (2021).

    Article  Google Scholar 

  97. Chen, S. et al. Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks. Nat. Electron. 3, 638–645 (2020).

    Article  Google Scholar 

  98. Nili, H. et al. Hardware-intrinsic security primitives enabled by analogue state and nonlinear conductance variations in integrated memristors. Nat. Electron. 1, 197–202 (2018).

    Article  Google Scholar 

  99. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  100. Mahmoodi, M. R., Prezioso, M. & Strukov, D. B. Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization. Nat. Commun. 10, 5113 (2019).

    Article  Google Scholar 

  101. Le Gallo, M. et al. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nat. Electron. 6, 680–693 (2023).

    Article  Google Scholar 

  102. Navaridas, J., Luján, M., Plana, L. A., Temple, S. & Furber, S. B. SpiNNaker: enhanced multicast routing. Parallel Comput. 45, 49–66 (2015).

    Article  Google Scholar 

  103. Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The SpiNNaker project. Proc. IEEE 102, 652–665 (2014).

    Article  Google Scholar 

  104. Painkras, E. et al. SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid-State Circuits 48, 1943–1953 (2013).

    Article  Google Scholar 

  105. Si, J. et al. A carbon-nanotube-based tensor processing unit. Nat. Electron. 7, 684–693 (2024).

    Article  Google Scholar 

  106. Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. In Proc. 44th Annual International Symposium on Computer Architecture 1–12 (ACM, 2017).

  107. Moons, B., Bankman, D. & Verhelst, M. in Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing (eds Moons, B. et al.) 55–58 (Springer International, 2019).

  108. Frenkel, C., Lefebvre, M., Legat, J. D. & Bol, D. A 0.086-mm2 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS. IEEE Trans. Biomed. Circuits Syst. 13, 145–158 (2019).

    Google Scholar 

  109. Kim, J., Kim, H., Huh, S., Lee, J. & Choi, K. Deep neural networks with weighted spikes. Neurocomputing 311, 373–386 (2018).

    Article  Google Scholar 

  110. Zeng, Y., Zhang, T. & Xu, B. Improving multi-layer spiking neural networks by incorporating brain-inspired rules. Sci. China Inf. Sci. 60, 052201 (2017).

    Article  Google Scholar 

  111. Neckar, A. et al. Braindrop: a mixed-signal neuromorphic architecture with a dynamical systems-based programming model. Proc. IEEE 107, 144–164 (2019).

    Article  Google Scholar 

  112. Thorpe, S., Delorme, A. & Van Rullen, R. Spike-based strategies for rapid processing. Neural Netw. 14, 715–725 (2001).

    Article  Google Scholar 

  113. Haddad, R., Lapid, H., Harel, D. & Sobel, N. Measuring smells. Curr. Opin. Neurobiol. 18, 438–444 (2008).

    Article  Google Scholar 

  114. Li, G. & Cleland, T. A. A two-layer biophysical model of cholinergic neuromodulation in olfactory bulb. J. Neurosci. 33, 3037 (2013).

    Article  Google Scholar 

  115. Linster, C. & Cleland, T. A. Decorrelation of odor representations via spike timing-dependent plasticity. Front. Comput. Neurosci. 4, 157 (2010).

    Google Scholar 

  116. Beyeler, M., Dutt, N. D. & Krichmar, J. L. Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule. Neural Netw. 48, 109–124 (2013).

    Article  Google Scholar 

  117. Izhikevich, E. M. Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb. Cortex 17, 2443–2452 (2007).

    Article  Google Scholar 

  118. Mozafari, M., Kheradpisheh, S. R., Masquelier, T., Nowzari-Dalini, A. & Ganjtabesh, M. First-spike-based visual categorization using reward-modulated STDP. IEEE Trans. Neural Netw. Learn. Syst. 29, 6178–6190 (2018).

    Article  Google Scholar 

  119. Szatmáry, B. & Izhikevich, E. M. Spike-timing theory of working memory. PLoS Comput. Biol. 6, e1000879 (2010).

    Article  MathSciNet  Google Scholar 

  120. Park, S. J., Lee, S. M., Oh, M.-H., Huh, Y. S. & Jang, H. W. Food quality assessment using chemoresistive gas sensors: achievements and future perspectives. Sustain. Food Technol. 2, 266–280 (2024).

    Article  Google Scholar 

  121. Jung, G. et al. Energy efficient artificial olfactory system with integrated sensing and computing capabilities for food spoilage detection. Adv. Sci. 10, 2302506 (2023).

    Article  Google Scholar 

  122. Zappa, D. Low-power detection of food preservatives by a novel nanowire-based sensor array. Foods 8, 226 (2019).

    Article  Google Scholar 

  123. Kim, S.-J., Choi, S.-J., Jang, J.-S., Cho, H.-J. & Kim, I.-D. Innovative nanosensor for disease diagnosis. Acc. Chem. Res. 50, 1587–1596 (2017).

    Article  Google Scholar 

  124. Shin, H. et al. Surface activity-tuned metal oxide chemiresistor: toward direct and quantitative halitosis diagnosis. ACS Nano 15, 14207–14217 (2021).

    Article  Google Scholar 

  125. Liu, J. J. et al. BreathSens: a continuous on-bed respiratory monitoring system with torso localization using an unobtrusive pressure sensing array. IEEE J. Biomed. Health Inform. 19, 1682–1688 (2015).

    Article  Google Scholar 

  126. Zhao, Y. The bottleneck and innovation key of MEMS-based metal oxide semiconductors gas sensor for petrochemical industry. Chem. Eng. J. 489, 151431 (2024).

    Article  Google Scholar 

  127. Jeong, S.-Y., Moon, Y. K., Wang, J. & Lee, J.-H. Exclusive detection of volatile aromatic hydrocarbons using bilayer oxide chemiresistors with catalytic overlayers. Nat. Commun. 14, 233 (2023).

    Article  Google Scholar 

  128. Sung, S.-H. et al. Data-centric artificial olfactory system based on the eigengraph. Nat. Commun. 15, 1211 (2024).

    Article  Google Scholar 

  129. Fahad, H. M. et al. Room temperature multiplexed gas sensing using chemical-sensitive 3.5-nm-thin silicon transistors. Sci. Adv. 3, e1602557 (2017).

    Article  Google Scholar 

  130. Son, M. et al. Real-time monitoring of geosmin and 2-methylisoborneol, representative odor compounds in water pollution using bioelectronic nose with human-like performance. Biosens. Bioelectron. 74, 199–206 (2015).

    Article  Google Scholar 

  131. Bishop, M. D. et al. Fabrication of carbon nanotube field-effect transistors in commercial silicon manufacturing facilities. Nat. Electron. 3, 492–501 (2020).

    Article  Google Scholar 

  132. Lau, J. H. in Chiplet Design and Heterogeneous Integration Packaging (ed. Lau, J. H.) 137–269 (Springer Nature Singapore, 2023).

  133. Xia, Q. et al. A state of the art review of through silicon vias: filling materials, filling processes, performance, and integration. Adv. Eng. Mater. 27, 2401799 (2025).

    Article  Google Scholar 

  134. Choi, K. et al. Bio-inspired ionic sensors: transforming natural mechanisms into sensory technologies. Nano–Micro Lett. 17, 180 (2025).

    Article  Google Scholar 

  135. Shepherd, G. M. Smell images and the flavour system in the human brain. Nature 444, 316–321 (2006).

    Article  Google Scholar 

  136. He, K., Wang, C., He, Y., Su, J. & Chen, X. Artificial neuron devices. Chem. Rev. 123, 13796–13865 (2023).

    Article  Google Scholar 

  137. Lledo, P.-M., Merkle, F. T. & Alvarez-Buylla, A. Origin and function of olfactory bulb interneuron diversity. Trends Neurosci. 31, 392–400 (2008).

    Article  Google Scholar 

  138. Gilbert, A. N., Martin, R. & Kemp, S. E. Cross-modal correspondence between vision and olfaction: the color of smells. Am. J. Psychol. 109, 335–351 (1996).

    Article  Google Scholar 

  139. Jeong, S.-Y., Kim, J.-S. & Lee, J.-H. Rational design of semiconductor-based chemiresistors and their libraries for next-generation artificial olfaction. Adv. Mater. 32, 2002075 (2020).

    Article  Google Scholar 

  140. Qin, C. et al. Artificial olfactory biohybrid system: an evolving sense of smell. Adv. Sci. 10, 2204726 (2023).

    Article  Google Scholar 

  141. Han, X. X., Rodriguez, R. S., Haynes, C. L., Ozaki, Y. & Zhao, B. Surface-enhanced Raman spectroscopy. Nat. Rev. Methods Primers 1, 87 (2022).

    Article  Google Scholar 

  142. Chen, S. et al. Wearable flexible microfluidic sensing technologies. Nat. Rev. Bioeng. 1, 950–971 (2023).

    Article  Google Scholar 

  143. Atencia, J. & Beebe, D. J. Controlled microfluidic interfaces. Nature 437, 648–655 (2005).

    Article  Google Scholar 

  144. Chaurasiya, R., Shih, L.-C., Chen, K.-T. & Chen, J.-S. Emerging higher-order memristors for bio-realistic neuromorphic computing: a review. Mater. Today 68, 356–376 (2023).

    Article  Google Scholar 

  145. Im, I. H., Kim, S. J. & Jang, H. W. Memristive devices for new computing paradigms. Adv. Intell. Syst. 2, 2000105 (2020).

    Article  Google Scholar 

  146. Li, Y., Wang, Z., Midya, R., Xia, Q. & Yang, J. J. Review of memristor devices in neuromorphic computing: materials sciences and device challenges. J. Phys. D Appl. Phys. 51, 503002 (2018).

    Article  Google Scholar 

  147. Choi, C. et al. Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence. Nat. Electron. 5, 386–393 (2022).

    Article  Google Scholar 

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

    Article  Google Scholar 

  149. Rueckauer, B. et al. NxTF: an API and compiler for deep spiking neural networks on Intel Loihi. J. Emerg. Technol. Comput. Syst. 18, 1–22 (2022).

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge funding from the National Natural Science Foundation of China (62571553), the Key Research and Development Plan of Shaanxi Province (2024GX-ZDCYL-01-06) and CNPC Basic and Forward-looking Science & Technology Program (2025DJ106).

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Authors and Affiliations

  1. State Key Laboratory of Oil and Gas Equipment, China National Petroleum Corporation Tubular Goods Research Institute (TGRI), Xi’an, P. R. China

    Yuxin Zhao & Juan Wang

  2. State Key Laboratory of Material Processing and Die and Mould Technology, Department of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China

    Shunping Zhang

  3. Beijing National Center for Condensed Matter Physics, Beijing Key Laboratory for Nanomaterials and Nanodevices, Institute of Physics, Chinese Academy of Sciences, Beijing, P. R. China

    Wenjie Liang

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  1. Yuxin Zhao
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  2. Juan Wang
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Y.Z. and J.W. made a substantial contribution to discussion of content, wrote and edited the manuscript before submission. S.Z. and W.L. made a substantial contribution to discussion of content and reviewed the manuscript before submission.

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Correspondence to Yuxin Zhao.

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Zhao, Y., Wang, J., Zhang, S. et al. Neuromorphic olfactory perception chips: towards universal odour recognition and cognition. Nat Rev Electr Eng 2, 755–772 (2025). https://doi.org/10.1038/s44287-025-00214-1

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