Accurately detecting a potential collision and triggering a timely escape response is critical in the field of robotics and autonomous vehicle safety. The lobula giant movement detector (LGMD) neuron in locusts can detect an approaching object and prevent collisions within a swarm of millions of locusts. This single neuronal cell performs nonlinear mathematical operations on visual stimuli to elicit an escape response with minimal energy expenditure. Collision avoidance models based on image processing algorithms have been implemented using analogue very-large-scale-integration designs, but none is as efficient as this neuron in terms of energy consumption or size. Here we report a nanoscale collision detector that mimics the escape response of the LGMD neuron. The detector comprises a monolayer molybdenum disulfide photodetector stacked on top of a non-volatile and programmable floating-gate memory architecture. It consumes a small amount of energy (in the range of nanojoules) and has a small device footprint (~1 µm × 5 µm).
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
The codes used for plotting the data are available from the corresponding author on reasonable request.
Wang, Y. & Frost, B. J. Time to collision is signalled by neurons in the nucleus rotundus of pigeons. Nature 356, 236–238 (1992).
Preuss, T., Osei-Bonsu, P. E., Weiss, S. A., Wang, C. & Faber, D. S. Neural representation of object approach in a decision-making motor circuit. J. Neurosci. 26, 3454–3464 (2006).
Maier, J. X., Neuhoff, J. G., Logothetis, N. K. & Ghazanfar, A. A. Multisensory integration of looming signals by rhesus monkeys. Neuron 43, 177–181 (2004).
Rind, F. C. Intracellular characterization of neurons in the locust brain signaling impending collision. J. Neurophysiol. 75, 986–995 (1996).
Fotowat, H. & Gabbiani, F. Collision detection as a model for sensory-motor integration. Annu. Rev. Neurosci. 34, 1–19 (2011).
Gray, J. R., Blincow, E. & Robertson, R. M. A pair of motion-sensitive neurons in the locust encode approaches of a looming object. J. Comp. Physiol. A 196, 927–938 (2010).
Tammero, L. F. & Dickinson, M. H. Collision-avoidance and landing responses are mediated by separate pathways in the fruit fly, Drosophila melanogaster. J. Exp. Biol. 205, 2785–2798 (2002).
Glantz, R. M. Defense reflex and motion detector responsiveness to approaching targets: the motion detector trigger to the defense reflex pathway. J. Comp. Physiol. 95, 297–314 (1974).
Oliva, D., Medan, V. & Tomsic, D. Escape behavior and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). J. Exp. Biol. 210, 865–880 (2007).
Poggio, T. & Reichardt, W. Considerations on models of movement detection. Kybernetik 13, 223–227 (1973).
Tanner, J. & Mead, C. in VLSI Signal Processing II Ch. 7 (IEEE, 1986).
Borst, A. Models of motion detection. Nat. Neurosci. 3, 1168 (2000).
Andreou, A. G. & Strohbehn, K. Analog VLSI implementation of the Hassenstein-Reichardt-Poggio models for vision computation. In Proc. 1990 IEEE International Conference on Systems, Man, and Cybernetics 707–710 (IEEE, 1990).
Harrison, R. R. & Koch, C. A robust analog VLSI motion sensor based on the visual system of the fly. Auton. Robots 7, 211–224 (1999).
Horridge, G. A. The compromise between seeing spatial layout and making visual discriminations. Curr. Sci. 60, 686–693 (1991).
Abbott, D. et al. New VLSI smart sensor for collision avoidance inspired by insect vision. In Proc. SPIE 2344 Intelligent Vehicle Highway Systems 105–115 (SPIE, 1995).
Moini, A. et al. An insect vision-based motion detection chip. IEEE J. Solid-State Circuits 32, 279–284 (1997).
Moini, A., Bouzerdoum, A., Yakovleff, A. & Eshraghian, K. Two-dimensional motion detector based on insect vision. In Proc. SPIE 2950 Advanced Focal Plane Arrays and Electronic Cameras 146–157 (SPIE, 1996).
Abbott, D. et al. Status of recent developments in collision avoidance using motion detectors based on insect vision. In Proc. SPIE 2902 Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS 242–247 (SPIE, 1997).
Okuno, H. & Yagi, T. A mixed analog–digital vision sensor for detecting objects approaching on a collision course. Rob. Autom. Syst. 57, 508–516 (2009).
Takami, R., Shimonomura, K., Kameda, S. & Yagi, T. An image pre-processing system employing neuromorphic 100/spl times/100 pixel silicon retina [robot vision applications]. In Proc. 2005 IEEE International Symposium on Circuits and Systems 2771–2774 (IEEE, 2005).
Harrison, R. R. A biologically inspired analog IC for visual collision detection. IEEE Trans. Circuits Syst. I 52, 2308–2318 (2005).
Sarkar, M., Bello, D. S. S., van Hoof, C. & Theuwissen, A. J. Biologically inspired CMOS image sensor for fast motion and polarization detection. IEEE Sens. J. 13, 1065–1073 (2012).
Zhang, C., Lindner, S., Antolovic, I. M., Wolf, M. & Charbon, E. A CMOS SPAD imager with collision detection and 128 dynamically reallocating TDCs for single-photon counting and 3D time-of-flight imaging. Sensors 18, 4016 (2018).
Baxter, J. A., Merced, D. A., Costinett, D. J., Tolbert, L. M. & Ozpineci, B. Review of electrical architectures and power requirements for automated vehicles. In Proc. 2018 IEEE Transportation Electrification Conference and Expo (ITEC) 944–949 (IEEE, 2018).
Herberholz, J. & Marquart, G. D. Decision making and behavioral choice during predator avoidance. Front. Neurosci. 6, 125 (2012).
Rind, F. C. & Bramwell, D. Neural network based on the input organization of an identified neuron signaling impending collision. J. Neurophysiol. 75, 967–985 (1996).
Gabbiani, F., Krapp, H. G., Koch, C. & Laurent, G. Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320–324 (2002).
Gabbiani, F., Krapp, H. G. & Laurent, G. Computation of object approach by a wide-field, motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999).
Badia, S. B. I., Bernardet, U. & Verschure, P. F. Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector. PLoS Comput. Biol. 6, e1000701 (2010).
Judge, S. & Rind, F. The locust DCMD, a movement-detecting neurone tightly tuned to collision trajectories. J. Exp. Biol. 200, 2209–2216 (1997).
Pearson, K., Heitler, W. & Steeves, J. Triggering of locust jump by multimodal inhibitory interneurons. J. Neurophysiol. 43, 257–278 (1980).
Rind, F. C. A chemical synapse between two motion detecting neurones in the locust brain. J. Exp. Biol. 110, 143–167 (1984).
Blanchard, M., Rind, F. C. & Verschure, P. F. M. J. Collision avoidance using a model of the locust LGMD neuron. Rob. Autom. Syst. 30, 17–38 (2000).
Fu, Q. et al. A visual neural network for robust collision perception in vehicle driving scenarios. In Proc. IFIP International Conference on Artificial Intelligence Applications and Innovations 67–79 (Springer, 2019).
Stafford, R., Santer, R. D. & Rind, F. C. A bio-inspired visual collision detection mechanism for cars: combining insect inspired neurons to create a robust system. Biosystems 87, 164–171 (2007).
Yue, S., Rind, F. C., Keil, M. S., Cuadri, J. & Stafford, R. A bio-inspired visual collision detection mechanism for cars: optimisation of a model of a locust neuron to a novel environment. Neurocomputing 69, 1591–1598 (2006).
Santer, R. D., Stafford, R. & Rind, F. C. Retinally-generated saccadic suppression of a locust looming-detector neuron: investigations using a robot locust. J. R. Soc. Interface 1, 61–77 (2004).
Cuadri, J., Linan, G., Stafford, R., Keil, M. & Roca, E. A bioinspired collision detection algorithm for VLSI implementation. In Proc SPIE 5839 Bioengineered and Bioinspired Systems II 238–248 (SPIE, 2005).
Lopez-Sanchez, O., Lembke, D., Kayci, M., Radenovic, A. & Kis, A. Ultrasensitive photodetectors based on monolayer MoS2. Nat. Nanotechnol. 8, 497–501 (2013).
Cappelletti, P., Golla, C., Olivo, P. & Zanoni, E. Flash Memories (Springer, 2013).
Lee, J. et al. Monolayer optical memory cells based on artificial trap-mediated charge storage and release. Nat. Commun. 8, 14734 (2017).
Arnold, A. J. et al. Mimicking neurotransmitter release in chemical synapses via hysteresis engineering in MoS2 transistors. ACS Nano 11, 3110–3118 (2017).
Sup Choi, M. et al. Controlled charge trapping by molybdenum disulphide and graphene in ultrathin heterostructured memory devices. Nat. Commun. 4, 1624 (2013).
Paul, T., Ahmed, T., Tiwari, K. K., Thakur, C. S. & Ghosh, A. A high-performance MoS2 synaptic device with floating gate engineering for neuromorphic computing. 2D Mater. 6, 045008 (2019).
Lundstrom, M. S. & Antoniadis, D. A. Compact models and the physics of nanoscale FETs. IEEE Trans. Electron Devices 61, 225–233 (2014).
Das, S., Dodda, A. & Das, S. A biomimetic 2D transistor for audiomorphic computing. Nat. Commun. 10, 3450 (2019).
Sebastian, A., Pannone, A., Radhakrishnan, S. S. & Das, S. Gaussian synapses for probabilistic neural networks. Nat. Commun. 10, 4199 (2019).
Tran, M. D. et al. Two-terminal multibit optical memory via van der Waals heterostructure. Adv. Mater. 31, 1807075 (2019).
Xuan, Y. et al. Multi-scale modeling of gas-phase reactions in metal-organic chemical vapor deposition growth of WSe2. J. Cryst. Growth 527, 125247 (2019).
Kang, K. et al. High-mobility three-atom-thick semiconducting films with wafer-scale homogeneity. Nature 520, 656–660 (2015).
Choudhury, T. H. et al. Chalcogen precursor effect on cold-wall gas-source chemical vapor deposition growth of WS2. Cryst. Growth Des. 18, 4357–4364 (2018).
Li, H. et al. From bulk to monolayer MoS2: evolution of Raman scattering. Adv. Funct. Mater. 22, 1385–1390 (2012).
Rumble, J. R. CRC Handbook of Chemistry and Physics 98th edn (CRC Press, 2018).
Crider, C. A., Poate, J. M., Rowe, J. E. & Sheng, T. T. Platinum silicide formation under ultrahigh-vacuum and controlled impurity ambients. J. Appl. Phys. 52, 2860–2868 (1981).
Das, S., Chen, H. Y., Penumatcha, A. V. & Appenzeller, J. High performance multilayer MoS2 transistors with scandium contacts. Nano Lett. 13, 100–105 (2013).
Schulman, D. S., Arnold, A. J. & Das, S. Contact engineering for 2D materials and devices. Chem. Soc. Rev. 47, 3037–3058 (2018).
This work was partially supported through grant number FA9550-17-1-0018 from the Air Force Office of Scientific Research (AFOSR) through the Young Investigator Program and Army Research Office (ARO) through contract no. W911NF1920338. We also acknowledge the support from the National Science Foundation (NSF) through the Pennsylvania State University’s 2D Crystal Consortium–Materials Innovation Platform (2DCC-MIP) under NSF cooperative agreement DMR-1539916.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Table 1, Figs. 1–18 and discussions 1–15.
Scenario depicting a car approaching the collision detector on a direct collision course.
Visual excitation experienced by the collision detector from an approaching car.
Scenario depicting a car approaching a wall and the corresponding visual excitation experienced by the collision detector from the reflected light.
Visual excitation due to increasing light intensity from a static source.
Simulated escape response from a multipixel collision detector array.
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Jayachandran, D., Oberoi, A., Sebastian, A. et al. A low-power biomimetic collision detector based on an in-memory molybdenum disulfide photodetector. Nat Electron 3, 646–655 (2020). https://doi.org/10.1038/s41928-020-00466-9
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