Abstract
The rapid growth of Internet of Things applications has substantially increased the number of connected sensors and data volume, yet conventional digital conversion and transmission systems impose high energy and latency costs. Here we develop a neuromorphic sensing system integrating a flexible piezoelectric haptic sensor array, event-triggered preprocessing circuitry and a memristive system on a chip. The circuitry transforms transient voltage spikes from sensor pixels into decaying voltage waveforms, generating a time surface for event-based analogue in-memory computing within the chip. Our system achieves 87%–92% recognition accuracy for patterns written on the sensor array and reduces the energy-delay product during inference compared with conventional digital platforms. These results highlight the potential of the memristive system on a chip for energy-efficient, low-latency edge processing of analogue sensor data, advancing intelligent sensing technologies.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to the full article PDF.
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The data that support the plots within this article, as well as other findings of this study, are available via Code Ocean at https://codeocean.com/capsule/4914834/tree/v1.
Code availability
The code that supports the results shown in this article is available via Code Ocean at https://codeocean.com/capsule/4914834/tree/v1. Code that supports the operation of the integrated chip for the demonstrated application is available from the corresponding author upon reasonable request.
References
Atzori, L., Iera, A. & Morabito, G. The Internet of Things: a survey. Comput. Netw. 54, 2787–2805 (2010).
Shi, W., Cao, J., Zhang, Q., Li, Y. & Xu, L. Edge computing: vision and challenges. IEEE Internet Things J. 3, 637–646 (2016).
Zhou, F. & Chai, Y. Near-sensor and in-sensor computing. Nat. Electron. 3, 664–671 (2020).
Hennessy, J. L. & Patterson, D. A. Computer Architecture: A Quantitative Approach (Elsevier, 2011).
Schuman, C. D. et al. A survey of neuromorphic computing and neural networks in hardware. Preprint at https://arxiv.org/abs/1705.06963 (2017).
Gallego, G. et al. Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44, 154–180 (2022).
Brandli, C., Berner, R., Yang, M., Liu, S. C. & Delbruck, T. A. 240 × 180 130 dB 3 μs latency global shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49, 2333–2341 (2014).
Posch, C., Matolin, D. & Wohlgenannt, R. A QVGA 143dB dynamic range asynchronous address-event PWM dynamic image sensor with lossless pixel-level video compression. In 2010 IEEE International Solid-State Circuits Conference 400–401 (IEEE, 2019).
Lichtsteiner, P. & Delbruck, T. A 64×64 AER logarithmic temporal derivative silicon retina. In 2005 PhD Research in Microelectronics and Electronics – Proceedings of the Conference II 406–409 (IEEE, 2005).
Event-based sensor IMX636. Prophesee https://www.prophesee.ai/event-based-sensor-imx636-sony-prophesee/ (2025).
Chen, S. & Guo, M. Live demonstration: CeleX-V: a 1M pixel multi-mode event-based sensor. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 1682–1683 (IEEE, 2019).
Liao, F. et al. Bioinspired in-sensor visual adaptation for accurate perception. Nat. Electron. 5, 84–91 (2022).
Zhou, F. et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019).
Sun, L. et al. In-sensor reservoir computing for language learning via two-dimensional memristors. Sci. Adv. 7, eabg1455 (2021).
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).
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).
Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020).
Jang, H. et al. In-sensor optoelectronic computing using electrostatically doped silicon. Nat. Electron. 5, 519–525 (2022).
Zhou, Y. et al. Computational event-driven vision sensors for in-sensor spiking neural networks. Nat. Electron. 6, 870–878 (2023).
Zhou, Y. et al. A 2T2R1C vision cell with 140 dB dynamic range and event-driven characteristics for in-sensor spiking neural network. In 2022 International Electron Devices Meeting 3141–3144 (IEEE, 2022).
Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).
Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).
Pi, S. et al. Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension. Nat. Nanotechnol. 14, 35–39 (2018).
Cai, F. et al. A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations. Nat. Electron. 2, 290–299 (2019).
Hu, M. et al. Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix–vector multiplication. In Proc. 53rd Annual Design Automation Conference 1–6 (IEEE, 2016).
Xia, Q. & Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019).
Yoon, J. H. et al. An artificial nociceptor based on a diffusive memristor. Nat. Commun. 9, 417 (2018).
Jiang, H. et al. Sub-10 nm Ta channel responsible for superior performance of a HfO2 memristor. Sci. Rep. 6, 28525 (2016).
Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).
Xia, Q. et al. Memristor–CMOS hybrid integrated circuits for reconfigurable logic. Nano Lett. 9, 3640–3645 (2009).
Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).
Wang, Z. et al. Reinforcement learning with analogue memristor arrays. Nat. Electron. 2, 115–124 (2019).
Li, C. et al. Long short-term memory networks in memristor crossbar arrays. Nat. Mach. Intell. 1, 49–57 (2019).
Li, C. et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nat. Commun. 9, 2385 (2018).
Li, C. et al. In-memory computing with memristor arrays. In 2018 IEEE 10th International Memory Workshop 1–4 (IEEE, 2018); https://doi.org/10.1109/IMW.2018.8388838
Dang, B. et al. Reconfigurable in-sensor processing based on a multi-phototransistor–one-memristor array. Nat. Electron. 7, 991–1003 (2024).
Huang, H. et al. Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing. Nat. Nanotechnol. 20, 93–103 (2025).
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E. & Benosman, R. B. HOTS: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1346–1359 (2017).
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X. & Benosman, R. HATS: histograms of averaged time surfaces for robust event-based object classification. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1731–1740 (IEEE, 2018).
Song, W. et al. Programming memristor arrays with arbitrarily high precision for analog computing. Science 383, 903–910 (2024).
Wang, Z. et al. Multi-diseases detection with memristive system on chip. Adv. Intell. Syst. 7, 2400736 (2024).
Huang, Y. et al. Radiofrequency signal processing with a memristive system-on-a-chip. Nat. Electron. 8, 587596 (2025).
Lloyd, S. P. Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982).
MacQueen, J. Some methods for classification and analysis of multivariate observations. In Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics Vol. 5.1 (eds Le Cam, L. M. & Neyman, J.) 281–298 (Univ. California Press, 1967).
Akopyan, F. et al. TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. 34.10, 1537–1557 (2015).
Pehle, C. et al. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front. Neurosci. 16, 795876 (2022).
Milano, G. et al. In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks. Nat. Mater. 21, 195–202 (2022).
Acknowledgements
Research in the USA is mainly sponsored by the National Science Foundation (CCF, grant no. 2133475, Q.X.), and in part by the Army Research Laboratory (grant no. W911NF-23-2-0014, Q.X.) and the Office of Naval Research (grant no. N00014-23-1-2021, Q.X.); research in Finland is mainly sponsored by the Research Council of Finland (grant no. 364467, S.M.). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the US Government. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Author information
Authors and Affiliations
Contributions
Q.X. and S.M. conceived and led the project. W.Z., Y.H., A.J.R. and A.Z. conducted work on the preprocessing circuitry and the SoC-based computing, A.T. and S.M. made the piezoelectric sensors. Q.X., N.G., J.J.Y. and M.H. contributed to the evaluation kit (hardware and software). W.Z., Y.H. and Q.X. wrote the article. All authors edited the paper before submission.
Corresponding author
Ethics declarations
Competing interests
Q.X. and J.J.Y. are cofounders and paid consultants of TetraMem. The other authors declare no competing interests.
Peer review
Peer review information
Nature Sensors thanks Laura Begon-Lours and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Piezoelectric sensor response under controlled pressure as measured by a MARK 10 pressure gauge.
a. Combined transient response of single pads across a pressure range (1.4-59.7 kPa). b. Transient response at 1.4 kPa. c. High-resolution transient waveform from 2 to 4 s. d Sensor sensitivity (voltage vs. pressure). e. Response time calculation. f. Sensor response test after bending (10.4% strain and 22.2% strain). g. Photograph of the bending test system set-up and when strain = 0% (Original Size:9.6 cm) and h. when strain = 10.4% and bending cycle = 1030 (Vertical change in size: 1 cm, Radius: 5.9 cm) and i. when strain = 10.4% and bending cycle = 1010. (Vertical change in size: 2 cm, radius: 4.2 cm).
Extended Data Fig. 2 An alternative, CNN-Based computing with the memristive SoC for the 4×4 sensor data recognition.
a. CNN-based computing. The combination of the sensor array and the connection circuitry converts the time-related trail information into the voltage amplitude. These pixels with different voltage amplitudes will be treated as images, and we train a CNN for that application. Different kernel information and the weight information in the fully connected layer are programmed into the memristive array for inference. b. CNN structure for this 3-letter recognition task c. the block division of the memristive array. d. Conductance map of the target and actual weight mapping of the CNN. e. Accuracy of the 3-letter recognition task. It reaches 100 percent accuracy for both software and hardware results.
Extended Data Fig. 3 An alternative, CNN-Based computing with the memristive SoC for the 32x32 handwritten digits classification.
a. For this task we use 2 convolutional layers and 1 fully connected layer structure with 2 max-pooling layers for the reduction of the dimensions. b. Block division of a single 248×256 memristive array for this CNN. c. Target and actual conductance map comparison for the weight mapping of both the convolutional and fully connected layers. d. Accuracy of the writing direction in the digit’s recognition task. We obtain 100% and 99.83% accuracy from the neural network-based computing method from software and hardware results separately.
Supplementary information
Supplementary Information
Supplementary Videos 1 and 2 captions, Table 1, Figs. 1–6, Notes 1–6 and References.
Supplementary Video 1
Sensor response under manual pressing. The video demonstrates the testing procedure used to obtain the piezoelectric sensor array’s response under repeated manual pressing.
Supplementary Video 2
Controlled press test using a MARK-10 pressure gauge. The video shows the procedure for obtaining the piezoelectric sensor array’s response under precisely controlled pressing conditions.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhao, W., Huang, Y., Tewari, A. et al. Event-based neuromorphic sensing system with flexible haptic sensors and a memristive system on a chip. Nat. Sens. (2026). https://doi.org/10.1038/s44460-025-00013-z
Received:
Accepted:
Published:
Version of record:
DOI: https://doi.org/10.1038/s44460-025-00013-z
