Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system

Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS2 and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS2-organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.

[1]  Pinshane Y. Huang,et al.  High-mobility three-atom-thick semiconducting films with wafer-scale homogeneity , 2015, Nature.

[2]  Stefano de Gironcoli,et al.  QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials , 2009, Journal of physics. Condensed matter : an Institute of Physics journal.

[3]  D. Jeong,et al.  Nonvolatile Memory Materials for Neuromorphic Intelligent Machines , 2018, Advanced materials.

[4]  Á. Nagy,et al.  Local-density-functional approximation for exchange-correlation potential , 1987 .

[5]  Donhee Ham,et al.  Vertical MoS2 Double-Layer Memristor with Electrochemical Metallization as an Atomic-Scale Synapse with Switching Thresholds Approaching 100 mV. , 2019, Nano letters.

[6]  M. Lukin,et al.  Electrical control of interlayer exciton dynamics in atomically thin heterostructures , 2018, Science.

[7]  Kresse,et al.  Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. , 1996, Physical review. B, Condensed matter.

[8]  Shimeng Yu,et al.  Optoelectronic resistive random access memory for neuromorphic vision sensors , 2019, Nature Nanotechnology.

[9]  Yonggang Huang,et al.  Two-dimensional materials in functional three-dimensional architectures with applications in photodetection and imaging , 2018, Nature Communications.

[10]  Woong Choi,et al.  Photoluminescence quenching in monolayer transition metal dichalcogenides by Al2O3 encapsulation , 2018, Applied Physics Letters.

[11]  S. Grimme,et al.  A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. , 2010, The Journal of chemical physics.

[12]  D. Ham,et al.  An Atomically Thin Optoelectronic Machine Vision Processor , 2020, Advanced materials.

[13]  Hao Wu,et al.  Few-layer molybdenum disulfide transistors and circuits for high-speed flexible electronics , 2014, Nature Communications.

[14]  J. Yang,et al.  Memristive crossbar arrays for brain-inspired computing , 2019, Nature Materials.

[15]  H.-S. Philip Wong,et al.  Face classification using electronic synapses , 2017, Nature Communications.

[16]  Takhee Lee,et al.  Intrinsic Optoelectronic Characteristics of MoS2 Phototransistors via a Fully Transparent van der Waals Heterostructure. , 2019, ACS nano.

[17]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[18]  W. Hu,et al.  A Ferroelectric/Electrochemical Modulated Organic Synapse for Ultraflexible, Artificial Visual‐Perception System , 2018, Advanced materials.

[19]  H-S Philip Wong,et al.  Artificial optic-neural synapse for colored and color-mixed pattern recognition , 2018, Nature Communications.

[20]  SungWoo Nam,et al.  Crumpled Graphene Photodetector with Enhanced, Strain‐Tunable, and Wavelength‐Selective Photoresponsivity , 2016, Advanced materials.

[21]  George G. Malliaras,et al.  Neuromorphic device architectures with global connectivity through electrolyte gating , 2017, Nature Communications.

[22]  C. David Wright,et al.  Role of Charge Traps in the Performance of Atomically Thin Transistors , 2017, Advanced materials.

[23]  D. Hamann.  Optimized norm-conserving Vanderbilt pseudopotentials , 2013, 1306.4707.

[24]  Juwon Lee,et al.  Monolayer optical memory cells based on artificial trap-mediated charge storage and release , 2017, Nature Communications.

[25]  Heung Cho Ko,et al.  A hemispherical electronic eye camera based on compressible silicon optoelectronics , 2008, Nature.

[26]  Fujio Izumi,et al.  VESTA: a three-dimensional visualization system for electronic and structural analysis , 2008 .

[27]  K Moon,et al.  RRAM-based synapse devices for neuromorphic systems. , 2019, Faraday discussions.

[28]  Paul F. Barbara,et al.  Intrinsic Charge Trapping in Organic and Polymeric Semiconductors: A Physical Chemistry Perspective , 2010 .

[29]  Takashi Taniguchi,et al.  Dissociation of two-dimensional excitons in monolayer WSe2 , 2018, Nature Communications.

[30]  G. Kukucska,et al.  Theoretical Investigation of Strain and Doping on the Raman Spectra of Monolayer MoS2 , 2017 .

[31]  Aude Oliva,et al.  Visual long-term memory has a massive storage capacity for object details , 2008, Proceedings of the National Academy of Sciences.

[32]  Wei D. Lu,et al.  Sparse coding with memristor networks. , 2017, Nature nanotechnology.

[33]  Weidong Zhou,et al.  Origami silicon optoelectronics for hemispherical electronic eye systems , 2017, Nature Communications.

[34]  Yong-Young Noh,et al.  Synthesis of ultrathin polymer insulating layers by initiated chemical vapour deposition for low-power soft electronics. , 2015, Nature materials.

[35]  David A. Strubbe,et al.  BerkeleyGW: A massively parallel computer package for the calculation of the quasiparticle and optical properties of materials and nanostructures , 2011, Comput. Phys. Commun..

[36]  Dmitry K. Polyushkin,et al.  Ultrafast machine vision with 2D material neural network image sensors , 2020, Nature.

[37]  Hua Zhang,et al.  Single-layer MoS2 phototransistors. , 2012, ACS nano.

[38]  Ji Hoon Kim,et al.  Wearable red–green–blue quantum dot light-emitting diode array using high-resolution intaglio transfer printing , 2015, Nature Communications.

[39]  Yei Hwan Jung,et al.  Stretchable silicon nanoribbon electronics for skin prosthesis , 2014, Nature Communications.

[40]  Jing Kong,et al.  Dielectric screening of excitons and trions in single-layer MoS2. , 2014, Nano letters.

[41]  C. A. Nelson,et al.  Correction to "charge transfer excitons at van der Waals interfaces". , 2015, Journal of the American Chemical Society.

[42]  Soon-Hong Kwon,et al.  Photon-triggered nanowire transistors. , 2017, Nature nanotechnology.

[43]  Young Min Song,et al.  An aquatic-vision-inspired camera based on a monocentric lens and a silicon nanorod photodiode array , 2020, Nature Electronics.

[44]  Robert J. Wood,et al.  Science, technology and the future of small autonomous drones , 2015, Nature.

[45]  Dae-Hyeong Kim,et al.  Multifunctional wearable devices for diagnosis and therapy of movement disorders. , 2014, Nature nanotechnology.

[46]  Moon J. Kim,et al.  Sub-10 nm Tunable Hybrid Dielectric Engineering on MoS2 for Two-Dimensional Material-Based Devices. , 2017, ACS nano.

[47]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[48]  Deji Akinwande,et al.  Two-dimensional flexible nanoelectronics , 2014, Nature Communications.

[49]  Yong‐Hoon Kim,et al.  Brain‐Inspired Photonic Neuromorphic Devices using Photodynamic Amorphous Oxide Semiconductors and their Persistent Photoconductivity , 2017, Advanced materials.

[50]  Yonggang Huang,et al.  Dynamically tunable hemispherical electronic eye camera system with adjustable zoom capability , 2011, Proceedings of the National Academy of Sciences.

[51]  Armantas Melianas,et al.  Organic electronics for neuromorphic computing , 2018, Nature Electronics.

[52]  Kaushik Roy,et al.  Towards spike-based machine intelligence with neuromorphic computing , 2019, Nature.

[53]  Wei Lu,et al.  The future of electronics based on memristive systems , 2018, Nature Electronics.

[54]  M. Kaltenbrunner,et al.  An ultra-lightweight design for imperceptible plastic electronics , 2013, Nature.

[55]  Christophe Bobda,et al.  Bio-inspired smart vision sensor: toward a reconfigurable hardware modeling of the hierarchical processing in the brain , 2020, Journal of Real-Time Image Processing.

[56]  Young Min Song,et al.  Human eye-inspired soft optoelectronic device using high-density MoS2-graphene curved image sensor array , 2017, Nature Communications.

[57]  Young Min Song,et al.  Bioinspired Artificial Eyes: Optic Components, Digital Cameras, and Visual Prostheses , 2018 .

[58]  Christophe Bobda,et al.  Event-Based Re-configurable Hierarchical Processors for Smart Image Sensors , 2019, 2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP).

[59]  Libai Huang,et al.  Highly mobile charge-transfer excitons in two-dimensional WS2/tetracene heterostructures , 2018, Science Advances.