Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing

Abstract Optically stimulated synaptic devices are critical to the development of neuromorphic computing with broad bandwidth and efficient interconnect. Although a few interesting materials have been employed to fabricate optically stimulated synaptic devices, the use of silicon (Si) that is the material of choice for very large-scale integration circuits in the conventional von Neumann computing has not been explored for optically stimulated synaptic devices. Here we take advantage of one of the most important nanostructures of Si — Si nanocrystals (NCs) to make synaptic devices, which can be effectively stimulated by light in the unprecedented broad spectral region from the ultraviolet to near-infrared, approaching the wavelength of ∼ 2 µm. These optically stimulated Si-NC-based synaptic devices demonstrate a series of important synaptic functionalities, well mimicking biological synapses. The plasticity of Si-NC-based synaptic devices originates from the dynamic trapping and release of photogenerated carriers at defects such as dangling bonds at the NC surface. The current facile use of Si NCs in broadband optoelectronic synaptic devices with low energy consumption has important implication for the large-scale deployment of Si in the emerging neuromorphic computing.

[1]  Kacper Pilarczyk,et al.  Synaptic Behavior in an Optoelectronic Device Based on Semiconductor‐Nanotube Hybrid , 2016 .

[2]  Ting Yu,et al.  Graphene Coupled with Silicon Quantum Dots for High‐Performance Bulk‐Silicon‐Based Schottky‐Junction Photodetectors , 2016, Advanced materials.

[3]  A. Rogach,et al.  Materials aspects of semiconductor nanocrystals for optoelectronic applications , 2017 .

[4]  Jing Guo,et al.  Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device. , 2017, ACS nano.

[5]  L. Abbott,et al.  Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.

[6]  R. Sinelnikov,et al.  From Hydrogen Silsesquioxane to Functionalized Silicon Nanocrystals , 2017 .

[7]  Hyunsang Hwang,et al.  Ultrasensitive artificial synapse based on conjugated polyelectrolyte , 2018, Nano Energy.

[8]  Jan Valenta,et al.  Probing silicon quantum dots by single-dot techniques , 2017, Nanotechnology.

[9]  P. Lu,et al.  Phosphorus Doping in Si Nanocrystals/SiO2 msultilayers and Light Emission with Wavelength compatible for Optical Telecommunication , 2016, Scientific Reports.

[10]  Jeslin J. Wu,et al.  Nonthermal Plasma Synthesis of Nanocrystals: Fundamental Principles, Materials, and Applications. , 2016, Chemical reviews.

[11]  Manuel Le Gallo,et al.  Stochastic phase-change neurons. , 2016, Nature nanotechnology.

[12]  Uwe R. Kortshagen,et al.  Highly efficient luminescent solar concentrators based on earth-abundant indirect-bandgap silicon quantum dots , 2017, Nature Photonics.

[13]  Mostafa Rahimi Azghadi,et al.  Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges , 2014, Proceedings of the IEEE.

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

[15]  M. Mitchell Waldrop,et al.  The chips are down for Moore’s law , 2016, Nature.

[16]  Yang Hui Liu,et al.  Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates , 2016, Advanced materials.

[17]  Deren Yang,et al.  Silicon-nanocrystal-incorporated ternary hybrid solar cells , 2016 .

[18]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

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

[20]  Minoru Fujii,et al.  All-inorganic colloidal silicon nanocrystals—surface modification by boron and phosphorus co-doping , 2016, Nanotechnology.

[21]  T. Nozaki,et al.  Silicon nanocrystal conjugated polymer hybrid solar cells with improved performance , 2014 .

[22]  Giacomo Indiveri,et al.  Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.

[23]  T. Nozaki,et al.  Controlled doping of silicon nanocrystals investigated by solution-processed field effect transistors. , 2014, ACS nano.

[24]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[25]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[26]  Zhengguo Xiao,et al.  Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices , 2016 .

[27]  Georges Kaddoum,et al.  Optical Communication in Space: Challenges and Mitigation Techniques , 2017, IEEE Communications Surveys & Tutorials.

[28]  T. Nozaki,et al.  Boron‐ and Phosphorus‐Hyperdoped Silicon Nanocrystals , 2015 .

[29]  Michael J Sailor,et al.  Biodegradable luminescent porous silicon nanoparticles for in vivo applications. , 2009, Nature materials.

[30]  Fabien Alibart,et al.  A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing , 2011, ArXiv.

[31]  Lief E. Fenno,et al.  The development and application of optogenetics. , 2011, Annual review of neuroscience.

[32]  Alexander L. Efros,et al.  Electronic Properties of Doped Semi-conductors , 1984 .

[33]  Jianhui Zhao,et al.  Memristor with Ag‐Cluster‐Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing , 2018 .

[34]  J. Grollier,et al.  A ferroelectric memristor. , 2012, Nature materials.

[35]  Harish Bhaskaran,et al.  On-chip photonic synapse , 2017, Science Advances.

[36]  Yi Ding,et al.  Comparative study on the localized surface plasmon resonance of boron- and phosphorus-doped silicon nanocrystals. , 2015, ACS nano.

[37]  Yihong Wu,et al.  An Optoelectronic Resistive Switching Memory with Integrated Demodulating and Arithmetic Functions , 2015, Advanced materials.

[38]  Shimeng Yu,et al.  Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.

[39]  Yi Ding,et al.  Ligand-Free, Colloidal, and Plasmonic Silicon Nanocrystals Heavily Doped with Boron , 2016 .

[40]  Carver A. Mead,et al.  A single-transistor silicon synapse , 1996 .

[41]  Barry P Rand,et al.  Extremely Low Operating Current Resistive Memory Based on Exfoliated 2D Perovskite Single Crystals for Neuromorphic Computing. , 2017, ACS nano.

[42]  Y. Liu,et al.  Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor , 2012 .

[43]  Cherie R. Kagan,et al.  Building devices from colloidal quantum dots , 2016, Science.

[44]  L. Wheeler,et al.  Thermodynamic Driving Force in the Spontaneous Formation of Inorganic Nanoparticle Solutions. , 2018, Nano letters.

[45]  Davide Mariotti,et al.  Type-I alignment in MAPbI3 based solar devices with doped-silicon nanocrystals , 2018, Nano Energy.

[46]  Shinhyun Choi,et al.  SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations , 2018, Nature Materials.

[47]  M. Stutzmann,et al.  Resonant electronic coupling enabled by small molecules in nanocrystal solids. , 2014, Nano letters.

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

[49]  Carrier transport in films of alkyl-ligand-terminated silicon nanocrystals , 2014, 1401.6713.

[50]  J. Joshua Yang,et al.  Synaptic electronics and neuromorphic computing , 2016, Science China Information Sciences.

[51]  Ming Liu,et al.  Light-Gated Memristor with Integrated Logic and Memory Functions. , 2017, ACS nano.

[52]  S. Maikap,et al.  Nanocrystals for silicon-based light-emitting and memory devices , 2013 .

[53]  D. Drachman Do we have brain to spare? , 2005, Neurology.

[54]  J. Yang,et al.  Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.

[55]  Deren Yang,et al.  Light-Emitting Diodes Based on Colloidal Silicon Quantum Dots with Octyl and Phenylpropyl Ligands. , 2018, ACS applied materials & interfaces.

[56]  Qing Wan,et al.  Artificial synapse network on inorganic proton conductor for neuromorphic systems. , 2014, Nature communications.

[57]  Weida Hu,et al.  Plasmonic Silicon Quantum Dots Enabled High-Sensitivity Ultrabroadband Photodetection of Graphene-Based Hybrid Phototransistors. , 2017, ACS nano.

[58]  Deren Yang,et al.  Density functional theory study on the B doping and B/P codoping of Si nanocrystals embedded in Si O 2 , 2017 .

[59]  Xiaodong Pi,et al.  Size‐Dependent Structures and Optical Absorption of Boron‐Hyperdoped Silicon Nanocrystals , 2016 .

[60]  W. Lu,et al.  Optogenetics-Inspired Tunable Synaptic Functions in Memristors. , 2018, ACS nano.

[61]  Rong Zhang,et al.  A light-stimulated synaptic device based on graphene hybrid phototransistor , 2017 .

[62]  Dimiter Prodanov,et al.  And Then There Was Light: Perspectives of Optogenetics for Deep Brain Stimulation and Neuromodulation , 2017, Front. Neurosci..

[63]  Junwei Wei,et al.  Synthesis of Ligand-Stabilized Silicon Nanocrystals with Size-Dependent Photoluminescence Spanning Visible to Near-Infrared Wavelengths , 2012 .

[64]  Jackson,et al.  Stretched-exponential relaxation arising from dispersive diffusion of hydrogen in amorphous silicon. , 1987, Physical review letters.

[65]  Pooi See Lee,et al.  A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnOx–Al2O3 thin film structure , 2016 .

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

[67]  Yan Yang,et al.  Duration of complex-spikes grades Purkinje cell plasticity and cerebellar motor learning , 2014, Nature.