Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Ultrafast machine vision with 2D material neural network image sensors

Nature volume 579, pages 62–66 (2020)Cite this article

Subjects

Abstract

Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN)1. The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed2,3,4,5,6,7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two-dimensional (2D) semiconductor8,9 photodiode10,11,12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Imaging ANN photodiode array.
Fig. 2: Implementation of the ANN photodiode array.
Fig. 3: Device operation as a classifier.
Fig. 4: Device operation as an autoencoder.
Fig. 5: Ultrafast image recognition.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

References

  1. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  ADS  CAS  Google Scholar 

  2. Mead, C. A. & Mahowald, M. A. A silicon model of early visual processing. Neural Netw. 1, 91–97 (1988).

    Article  Google Scholar 

  3. Lichtsteiner, P., Posch, C. & Delbruck, T. A. 128×128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circuits 43, 566–576 (2008).

    Article  ADS  Google Scholar 

  4. Cottini, N., Gottardi, M., Massari, N., Passerone, R. & Smilansky, Z. A. 33 μW 64×64 pixel vision sensor embedding robust dynamic background subtraction for EVENT detection and scene interpretation. IEEE J. Solid-State Circuits 48, 850–863 (2013).

    Article  ADS  Google Scholar 

  5. Kyuma, K. et al. Artificial retinas—fast, versatile image processors. Nature 372, 197–198 (1994).

    Article  ADS  CAS  Google Scholar 

  6. Posch, C., Serrano-Gotarredona, T., Linares-Barranco, B. & Delbruck, T. Retinomorphic event-based vision sensors: bioinspired cameras with spiking output. Proc. IEEE 102, 1470–1484 (2014).

    Article  Google Scholar 

  7. Zhou, F. et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019).

    Article  CAS  Google Scholar 

  8. Manzeli, S., Ovchinnikov, D., Pasquier, D., Yazyev, O. V. & Kis, A. 2D transition metal dichalcogenides. Nat. Rev. Mater. 2, 17033 (2017).

    Article  ADS  CAS  Google Scholar 

  9. Mueller, T. & Malic, E. Exciton physics and device application of two-dimensional transition metal dichalcogenide semiconductors. npj 2D Mater. Appl. 2, 29 (2018).

    Article  Google Scholar 

  10. Pospischil, A., Furchi, M. M. & Mueller, T. Solar-energy conversion and light emission in an atomic monolayer p–n diode. Nat. Nanotechnol. 9, 257–261 (2014).

    Article  ADS  CAS  Google Scholar 

  11. Baugher, B. W., Churchill, H. O., Yang, Y. & Jarillo-Herrero, P. Optoelectronic devices based on electrically tunable p–n diodes in a monolayer dichalcogenide. Nat. Nanotechnol. 9, 262–267 (2014).

    Article  ADS  CAS  Google Scholar 

  12. Ross, J. S. et al. Electrically tunable excitonic light-emitting diodes based on monolayer WSe2 p–n junctions. Nat. Nanotechnol. 9, 268–272 (2014).

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  14. Sheridan, P. M. et al. Sparse coding with memristor networks. Nat. Nanotechnol. 12, 784–789 (2017).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  16. Kim, K. H. et al. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett. 12, 389–395 (2012).

    Article  ADS  CAS  Google Scholar 

  17. Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).

    Article  ADS  CAS  Google Scholar 

  18. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    Article  ADS  MathSciNet  CAS  Google Scholar 

  19. Hamerly, R., Bernstein, L., Sludds, A., Soljačić, M. & Englund, D. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X 9, 021032 (2019).

    CAS  Google Scholar 

  20. Psaltis, D., Brady, D., Gu, X. G. & Lin, S. Holography in artificial neural networks. Nature 343, 325–330 (1990).

    Article  ADS  CAS  Google Scholar 

  21. Kolb, H. How the retina works: much of the construction of an image takes place in the retina itself through the use of specialized neural circuits. Am. Sci. 91, 28–35 (2003).

    Article  Google Scholar 

  22. Jeong, K.-H., Kim, J. & Lee, L. P. Biologically inspired artificial compound eyes. Science 312, 557–561 (2006).

    Article  ADS  CAS  Google Scholar 

  23. Choi, C. et al. Human eye-inspired soft optoelectronic device using high-density MoS2-graphene curved image sensor array. Nat. Commun. 8, 1664 (2017).

    Article  ADS  Google Scholar 

  24. Schwarte, R. et al. New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device (PMD). In Proc. SPIE Sensors, Sensor Systems, and Sensor Data Processing Vol. 3100, 245–253 (SPIE, 1997).

  25. Sugeta, T., Urisu, T., Sakata, S. & Mizushima, Y. Metal-semiconductor-metal photodetector for high-speed optoelectronic circuits. Jpn. J. Appl. Phys. 19, 459 (1980).

    Article  Google Scholar 

  26. Wachter, S., Polyushkin, D. K., Bethge, O. & Mueller, T. A microprocessor based on a two-dimensional semiconductor. Nat. Commun. 8, 14948 (2017).

    Article  ADS  CAS  Google Scholar 

  27. Goossens, S. et al. Broadband image sensor array based on graphene–CMOS integration. Nat. Photon. 11, 366–371 (2017).

    Article  ADS  CAS  Google Scholar 

  28. Bishop, C. M. Training with noise is equivalent to Tikhonov regularization. Neural Comput. 7, 108–116 (1995).

    Article  Google Scholar 

  29. Bengio, Y. in Neural Networks: Tricks of the Trade Vol. 7700 (eds Montavon G. et al.) 437–478 (Springer, 2012).

  30. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Article  ADS  Google Scholar 

  31. Massicotte, M. et al. Dissociation of two-dimensional excitons in monolayer WSe2. Nat. Commun. 9, 1633 (2018).

    Article  ADS  Google Scholar 

  32. Li, D. et al. Two-dimensional non-volatile programmable p–n junctions. Nat. Nanotechnol. 12, 901–906 (2017).

    Article  ADS  CAS  Google Scholar 

  33. Lv, L. et al. Reconfigurable two-dimensional optoelectronic devices enabled by local ferroelectric polarization. Nat. Commun. 10, 3331 (2019).

    Article  ADS  Google Scholar 

  34. Bertolazzi, S., Krasnozhon, D. & Kis, A. Nonvolatile memory cells based on MoS2/graphene heterostructures. ACS Nano 7, 3246–3252 (2013).

    Article  CAS  Google Scholar 

  35. Salakhutdinov, R. & Hinton, G. Semantic hashing. Int. J. Approx. Reason. 50, 969–978 (2009).

    Article  Google Scholar 

  36. Castellanos-Gomez, A. et al. Deterministic transfer of two-dimensional materials by all-dry viscoelastic stamping. 2D Mater. 1, 011002 (2014).

    Article  Google Scholar 

  37. Gerchberg, R. W. & Saxton, W. O. A practical algorithm for the determination of the phase from image and diffraction plane pictures. Optik 35, 237–246 (1972).

    Google Scholar 

Download references

Acknowledgements

We thank B. Limbacher for discussions and A. Kleinl, M. Paur and F. Dona for technical assistance. We acknowledge financial support by the Austrian Science Fund FWF (START Y 539-N16) and the European Union (grant agreement number 785219 Graphene Flagship and Marie Sklodowska-Curie Individual Fellowship OPTOvanderWAALS, grant ID 791536).

Author information

Authors and Affiliations

  1. Institute of Photonics, Vienna University of Technology, Vienna, Austria

    Lukas Mennel, Joanna Symonowicz, Stefan Wachter, Dmitry K. Polyushkin, Aday J. Molina-Mendoza & Thomas Mueller

Authors
  1. Lukas Mennel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joanna Symonowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Wachter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dmitry K. Polyushkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aday J. Molina-Mendoza
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas Mueller
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.M. conceived the experiment. L.M. designed and built the experimental setup, programmed the machine-learning algorithms, carried out the measurements and analysed the data. J.S. fabricated the ANN vision sensor. S.W. and D.K.P. contributed to the sample fabrication. A.J.M.-M. fabricated and characterized the floating-gate detector. L.M., J.S. and T.M. prepared the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Lukas Mennel or Thomas Mueller.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Yang Chai, Frank Koppens and Sangyoun Lee for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Photodiode array uniformity.

Gate tunability of the responsivities of all 27 photodetectors. One of the detector elements (pixel 7, subpixel 2) did not show any response to light (due to a broken electrical wire), which, however, had no crucial influence on the overall system performance.

Extended Data Fig. 2 Photodiode characteristics.

a, Current–voltage characteristic curve under dark (blue) and illuminated (green) conditions. The series resistance Rs and shunt resistance Rsh are ~106 Ω and 109 Ω, respectively. For zero-bias operation, we estimate a noise-equivalent power of NEP = Ith/R ≈ 10−13 W Hz−1/2, where R ≈ 60 mA W−1 is the (maximum) responsivity and \({I}_{{\rm{th}}}=\sqrt{4{k}_{{\rm{B}}}T\Delta f/{R}_{{\rm{sh}}}}\) the thermal noise, where kB is the Boltzmann constant, Δf is the bandwidth and T is the temperature. b, Dependence of the short-circuit photocurrent on the light intensity for different split-gate voltages. Importantly, the response is linear (I ∝ P), as assumed in equation (1).

Extended Data Fig. 3

Circuit of the ANN photodiode array.

Extended Data Fig. 4 Experimental setup.

a, Experimental setup for training the classifier and the autoencoder. CW, continuous wave. b, Experimental setup for time-resolved measurements. TIA, transimpedance amplifier. A pulse generator triggers the pulsed laser as well as the oscilloscope. c, Photograph of the optical setup (for schematic see Fig. 2d). d, Flow chart of the training algorithm. The blue shaded boxes are interactions with the ANN photodiode array.

Extended Data Fig. 5 Classifier training.

Photoresponsivity values of all 27 photodetectors with σ = 0.3 training data. a, b, Initial (a) and epoch 30 (b) responsivity values. The weights for the σ = 0.2 and σ = 0.4 training data are similar. c, Measured currents over all epochs for a specific projected letter and at all three noise levels. d, Histogram of the initial and final responsivity values for the three different noise levels.

Extended Data Fig. 6 Comparison with computer simulation.

Classifier training of the analogue vision sensor (solid lines) and simulation of the system on a computer (dashed lines) for different data noise levels σ. The same ANN architecture, input data, effective learning rate and starting weights have been used. The same accuracy and loss are eventually reached after training. The slightly slower convergence of the analogue implementation compared with the simulation reflects the nonidealities (defective subpixel, device-to-device variations) of the former. Further discussion on the impact of nonidealities is provided in Extended Data Fig. 10.

Extended Data Fig. 7 Training datasets.

a, b, Dataset of 30 epochs of classifier (a) and autoencoder training (b) with a test data noise level of σ = 0.4 and σ = 0.15 respectively.

Extended Data Fig. 8 Autoencoder photoresponsivities/weights.

a, b, Initial (a) and epoch 30 (b) encoder photoresponsivity values (left) and decoder weights (right).

Extended Data Fig. 9 Floating-split-gate photodiode with memory.

a, Schematic of the floating gate photodiode. The addition of 2-nm-thick Au layers, sandwiched between Al2O3 and hexagonal boron nitride (hBN), enables the storage of electric charge when a gate voltage is applied to the device, acting as a floating-gate memory. b, Electronic characteristic curves of the photodiode operated in p–n, n–p and p–p configurations. c, The ability of the device to ‘remember’ the previous configuration can be verified from the time-resolved photocurrent measurement. The measurement is performed as follows: the back-gate voltages are set to VG1 = +5 V and VG2 = −5 V and are then disconnected, that is, there is no longer an applied gate voltage and the only electric field is that generated by the charge stored on the floating electrodes. The short-circuit photocurrent is then measured upon optical illumination. The light is then switched off, at ~1,100 s, with a corresponding drop of the photocurrent to zero. After ~1,600 s, the light is switched on again, causing the current to reach its initial value, and then a smaller value when the intensity of the light is reduced (~1,700 s). After ~2,300 s, the opposite voltage configuration is applied to the back gates (VG1 = −5 V and VG2 = +5 V), inducing a polarity inversion that also remains permanent. Now, a positive photocurrent (red line) is obtained.

Extended Data Fig. 10 Robustness of the network.

a, Detector uniformity, extracted from Extended Data Fig. 1. The fitted Gaussian probability distribution has a standard deviation of σ = 0.205 (40 mA W−1 V−1). b, Monte Carlo simulation of a vision sensor with detector responsivities of a given standard deviation. (The photodetectors of the actual device have a measured photoresponsivity standard deviation of 0.205.) Trained on the MNIST database of handwritten digits, the classifier has 784 pixels and 10 subpixels per pixel. For each data point, 50 random photoresponsivity variations were evaluated. c, Accuracy dependence on the number of (randomly chosen) defective subpixels. The same ANN and Monte Carlo simulation scheme as in b were used. For each data point, 50 random sets of modified photoresponsivities were evaluated.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mennel, L., Symonowicz, J., Wachter, S. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020). https://doi.org/10.1038/s41586-020-2038-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2038-x

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing