Monolithic cell-on-memristor architecture enables wafer-scale integration of oscillatory chemoreceptors for bio-realistic gustatory chips
Source abstract
Abstract
Array fabrication and the wafer-scale integration of artificial oscillatory chemoreceptors are crucial for enabling biomimetic chips with bio-realistic chemoreception in practical bioapplications. However, existing chemoreceptors based on conventional architectures require sophisticated or non-scalable fabrication techniques due to inherent material or structural defects. Here we introduce a monolithic cell-on-memristor (CoM) chemoreceptive architecture featuring a unique oscillation mechanism for self-powered biosensing and in situ spike encoding. Through rational material selection and complementary metal-oxide-semiconductor-compatible fabrication, we realize the demonstration of a wafer-scale 10 × 10 CoM array with a spatial resolution of 51 pixels per inch and a very small pixel size of 150 μm, with potential for further scaling down. Using its bio-plausible ion-modulated voltage oscillations with spatiotemporal probabilistic spiking information, we exploit the CoM-array-based gustatory chip to replicate gustation for accuracy salty taste classification. Our CoM architecture offers a general and scalable approach for implementing chemoreceptive oscillatory systems aimed at human-machine biointegration applications.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Nonlinear Dynamics and Pattern Formation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
Show source-provided references
1. Wang, W. et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science 380, 735–742 (2023).10.1126/science.ade0086 ↗
2. Chen, L. et al. Spike timing-based coding in neuromimetic tactile system enables dynamic object classification. Science 384, 660–665 (2024).10.1126/science.adf3708 ↗
3. Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).10.1126/science.aao0098 ↗
4. Wang, X. et al. Vertically integrated spiking cone photoreceptor arrays for color perception. Nat. Commun. 14, 3444 (2023).10.1038/s41467-023-39143-8 ↗
5. Li, Z. et al. Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system. Nat. Commun. 15, 7275 (2024).10.1038/s41467-024-51609-x ↗
6. Li, F. et al. An artificial visual neuron with multiplexed rate and time-to-first-spike coding. Nat. Commun. 15, 3689 (2024).10.1038/s41467-024-48103-9 ↗
7. He, K., Wang, C., He, Y., Su, J. & Chen, X. Artificial neuron devices. Chem. Rev. 123, 13796–13865 (2023).10.1021/acs.chemrev.3c00527 ↗
8. Liu, H. et al. Artificial neuronal devices based on emerging materials: neuronal dynamics and applications. Adv. Mater. 35, e2205047 (2023).10.1002/adma.202205047 ↗
9. Xu, Y. T. et al. A nanofluidic spiking synapse. Proc. Natl Acad. Sci. USA 121, e2403143121 (2024).10.1073/pnas.2403143121 ↗
10. Sarkar, T. et al. An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing. Nat. Electron. 5, 774–783 (2022).10.1038/s41928-022-00859-y ↗
11. Harikesh, P. C. et al. Organic electrochemical neurons and synapses with ion mediated spiking. Nat. Commun. 13, 901 (2022).10.1038/s41467-022-28483-6 ↗
12. Harikesh, P. C. et al. Ion-tunable antiambipolarity in mixed ion-electron conducting polymers enables biorealistic organic electrochemical neurons. Nat. Mater. 22, 242–248 (2023).10.1038/s41563-022-01450-8 ↗
13. Matrone, G. M. et al. A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways. Nat. Commun. 15, 2868 (2024).10.1038/s41467-024-47226-3 ↗
14. Belleri, P. et al. Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics. Nat. Commun. 15, 5350 (2024).10.1038/s41467-024-49668-1 ↗
15. Gkoupidenis, P. et al. Organic mixed conductors for bioinspired electronics. Nat. Rev. Mater. 9, 134–149 (2023).10.1038/s41578-023-00622-5 ↗
16. Harikesh, P. C., Tu, D. & Fabiano, S. Organic electrochemical neurons for neuromorphic perception. Nat. Electron. 7, 525–536 (2024).10.1038/s41928-024-01200-5 ↗
17. Xu, G. et al. Nanofluidic ionic memristors. ACS Nano 18, 19423–19442 (2024).
18. Han, J. K., Park, S. C., Yu, J. M., Ahn, J. H. & Choi, Y. K. A bioinspired artificial gustatory neuron for a neuromorphic based electronic tongue. Nano Lett. 22, 5244–5251 (2022).10.1021/acs.nanolett.2c01107 ↗
19. Georgiou, P., Constandinou, T. G. & Toumazou, C. Low-power spiking chemical pixel sensor. Electron. Lett. 42, 1331–1332 (2006).10.1049/el:20062836 ↗
20. 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).10.1038/s41467-023-39430-4 ↗
21. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).10.1038/s41586-020-1942-4 ↗
22. Zhang, W. et al. Edge learning using a fully integrated neuro-inspired memristor chip. Science 381, 1205–1211 (2023).10.1126/science.ade3483 ↗
23. Kumar, A. et al. Recent advancements in metal oxide-based hybrid nanocomposite resistive random-access memories for artificial intelligence. InfoMat 7, e12644 (2024).10.1002/inf2.12644 ↗
24. Chu, M. et al. Neuromorphic hardware system for visual pattern recognition with memristor array and cmos neuron. IEEE Trans. Ind. Electron. 62, 2410–2419 (2015).10.1109/TIE.2014.2356439 ↗
25. Choi, C. et al. Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence. Nat. Electron. 5, 386–393 (2022).10.1038/s41928-022-00778-y ↗
26. Ren, H. et al. An ion-mediated spiking chemical neuron based on Mott memristor. Adv. Mater. 36, 2403678 (2024).10.1002/adma.202403678 ↗
27. Xue, M. et al. Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing. Nat. Commun. 13, 5064 (2022).10.1038/s41467-022-32749-4 ↗
28. Douthwaite, M., Moser, N. & Georgiou, P. CMOS ISFET arrays for integrated electrochemical sensing and imaging applications: a tutorial. IEEE Sens. J. 21, 22155–22169 (2021).10.1109/JSEN.2021.3094206 ↗
29. Ohayon, D. et al. Biofuel powered glucose detection in bodily fluids with an n-type conjugated polymer. Nat. Mater. 19, 456–463 (2020).10.1038/s41563-019-0556-4 ↗
30. Witt, M. in Smell and Taste (ed. Doty, R. L.) 147–171 (Elsevier, 2019).
31. Nomura, K., Nakanishi, M., Ishidate, F., Iwata, K. & Taruno, A. All-electrical Ca2+-independent signal transduction mediates attractive sodium taste in taste buds. Neuron 106, 816–829 (2020).10.1016/j.neuron.2020.03.006 ↗
32. Bousoulas, P. et al. Investigating the origins of ultra-short relaxation times of silver filaments in forming-free SiO2-based conductive bridge memristors. Nanotechnology 31, 454002 (2020).10.1088/1361-6528/aba3a1 ↗
33. Lengler, J. & Steger, A. Note on the coefficient of variations of neuronal spike trains. Biol. Cybern. 111, 229–235 (2017).10.1007/s00422-017-0717-y ↗
34. Chandrashekar, J. et al. The cells and peripheral representation of sodium taste in mice. Nature 464, 297–301 (2010).10.1038/nature08783 ↗
35. Smith, D. V., John, S. J. & Boughter, J. D. Neuronal cell types and taste quality coding. Physiol. Behav. 69, 77–85 (2000).10.1016/S0031-9384(00)00190-6 ↗
36. Taruno, A. & Gordon, M. D. Molecular and cellular mechanisms of salt taste. Annu. Rev. Physiol. 85, 25–45 (2023).10.1146/annurev-physiol-031522-075853 ↗
37. Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).10.1038/s41467-018-07052-w ↗
38. Di Lorenzo, P. M. & Victor, J. D. Taste response variability and temporal coding in the nucleus of the solitary tract of the rat. J. Neurophysiol. 90, 1418–1431 (2003).10.1152/jn.00177.2003 ↗
39. Pannone, A. et al. Robust chemical analysis with graphene chemosensors and machine learning. Nature 634, 572–578 (2024).10.1038/s41586-024-08003-w ↗
40. van Doremaele, E. R. W., Ji, X., Rivnay, J. & van de Burgt, Y. A retrainable neuromorphic biosensor for on-chip learning and classification. Nat. Electron. 6, 765–770 (2023).10.1038/s41928-023-01020-z ↗
41. Wang, C. et al. Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor arrays. Nat. Electron. 7, 157–167 (2024).10.1038/s41928-023-01107-7 ↗
42. Li, Y. et al. Machine learning-assisted wearable sensor array for comprehensive ammonia and nitrogen dioxide detection in wide relative humidity range. InfoMat 6, e12544 (2024).10.1002/inf2.12544 ↗
43. Chicca, E. & Indiveri, G. A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems. Appl. Phys. Lett. 116, 120501 (2020).10.1063/1.5142089 ↗
44. Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019).10.1038/s41586-019-1677-2 ↗
45. Wu, Y., Deng, L., Li, G., Zhu, J. & Shi, L. Spatio-temporal backpropagation for training high-performance spiking neural networks. Front. Neurosci. 12, 331 (2018).10.3389/fnins.2018.00331 ↗
46. Bigiani, A. The origin of saltiness: oral detection of NaCl. Curr. Opin. Physiol. 19, 156–161 (2021).10.1016/j.cophys.2020.11.006 ↗
47. Webb, J., Bolhuis, D. P., Cicerale, S., Hayes, J. E. & Keast, R. The relationships between common measurements of taste function. Chemosens. Percept. 8, 11–18 (2015).10.1007/s12078-015-9183-x ↗
48. Kelling, S. T. & Halpern, B. P. Taste judgements and gustatory stimulus-duration: taste quality, taste intensity, and reaction-time. Chem. Senses 13, 559–586 (1988).10.1093/chemse/13.4.559 ↗
49. Zhu, K. et al. Hybrid 2D-CMOS microchips for memristive applications. Nature 618, 57–62 (2023).10.1038/s41586-023-05973-1 ↗
50. Qin, X. et al. Manufacturing high-performance flexible sensors via advanced patterning techniques. Int. J. Extrem. Manuf. 7, 032003 (2025).10.1088/2631-7990/ada857 ↗
51. Liu, M. et al. A 16.4kPixel 3.08-to-3.86 THz digital real-time CMOS image sensor with 73 dB dynamic range. In Proc. 2023 IEEE International Solid-State Circuits Conference 4–6 (IEEE, 2023).10.1109/ISSCC42615.2023.10067620 ↗
52. Zhang, H. H. et al. A 128 × 128 monolithic spike-based hybrid-vision sensor with 0.96 Geps and 117 kfps. J. Semicond. 46, 092201 (2025).10.1088/1674-4926/25020010 ↗
53. Xia, Q. & Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019).10.1038/s41563-019-0291-x ↗