Atomic scale nanoelectronics for quantum neuromorphic devices: comparing different materials

I review the advancements of atomic scale nanoelectronics towards quantum neuromorphics. First, I summarize the key properties of elementary combinations of few neurons, namely long-- and short--term plasticity, spike-timing dependent plasticity (associative plasticity), quantumness and stochastic effects, and their potential computational employment. Next, I review several atomic scale device technologies developed to control electron transport at the atomic level, including single atom implantation for atomic arrays and CMOS quantum dots, single atom memories, Ag$_2$S and Cu$_2$S atomic switches, hafnium based RRAMs, organic material based transistors, Ge$_2$Sb$_2$Te$_5$ synapses. Each material/method proved successful in achieving some of the properties observed in real neurons. I compare the different methods towards the creation of a new generation of naturally inspired and biophysically meaningful artificial neurons, in order to replace the rigid CMOS based neuromorphic hardware. The most challenging aspect to address appears to obtain both the stochastic/quantum behavior and the associative plasticity, which are currently observed only below and above 20 nm length scale respectively, by employing the same material.

[1]  F Bermúdez-Rattoni Long-Term Potentiation and Depression as Putative Mechanisms for Memory Formation -- Neural Plasticity and Memory: From Genes to Brain Imaging , 2007 .

[2]  W. Maass,et al.  State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.

[3]  Y. Maeda,et al.  A pulse-type hardware neuron model with beating, bursting excitation and plateau potential. , 2000, Bio Systems.

[4]  P. J. Sjöström,et al.  Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.

[5]  Giorgio Ferrari,et al.  Giant random telegraph signal generated by single charge trapping in submicron n-metal-oxide-semiconductor field-effect transistors , 2008 .

[6]  Arif Babul,et al.  Neuron dynamics in the presence of 1/f noise. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Wulfram Gerstner,et al.  A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.

[8]  Romain Wacquez,et al.  Few electron limit of n-type metal oxide semiconductor single electron transistors , 2012, Nanotechnology.

[9]  Giorgio Ferrari,et al.  Anderson-Mott transition in arrays of a few dopant atoms in a silicon transistor. , 2012, Nature nanotechnology.

[10]  Alessandro Calderoni,et al.  Microwave irradiation effects on random telegraph signal in a MOSFET , 2007 .

[11]  Rufin van Rullen,et al.  Neurons Tune to the Earliest Spikes Through STDP , 2005, Neural Computation.

[12]  Luca Larcher,et al.  Random Telegraph Signal noise properties of HfOx RRAM in high resistive state , 2012, 2012 Proceedings of the European Solid-State Device Research Conference (ESSDERC).

[13]  Shigeo Sato,et al.  Neuromorphic quantum computation with energy dissipation , 2005 .

[14]  M. Teich,et al.  Fractal character of the neural spike train in the visual system of the cat. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  Stefano Ambrogio,et al.  Noise-Induced Resistance Broadening in Resistive Switching Memory—Part I: Intrinsic Cell Behavior , 2015, IEEE Transactions on Electron Devices.

[16]  廣瀬雄一,et al.  Neuroscience , 2019, Workplace Attachments.

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

[18]  Arno Villringer,et al.  Reversed timing-dependent associative plasticity in the human brain through interhemispheric interactions. , 2013, Journal of neurophysiology.

[19]  Luc Berthouze,et al.  Role of STDP and heterogeneity in the emergence of long-range temporal correlations and frequency scaling in networks of LIF neurons , 2010, BMC Neuroscience.

[20]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[21]  Massimiliano Di Ventra,et al.  Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements , 2010, Proceedings of the IEEE.

[22]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[23]  Wolfgang Maass,et al.  Noise as a Resource for Computation and Learning in Networks of Spiking Neurons , 2014, Proceedings of the IEEE.

[24]  S. Ambrogio,et al.  Spike-timing dependent plasticity in a transistor-selected resistive switching memory , 2013, Nanotechnology.

[25]  Eric R Kandel,et al.  Bidirectional Regulation of Hippocampal Long-Term Synaptic Plasticity and Its Influence on Opposing Forms of Memory , 2010, The Journal of Neuroscience.

[26]  E. Scheer,et al.  A current-driven single-atom memory. , 2013, Nature nanotechnology.

[27]  M. Fanciulli,et al.  Charge dynamics of a single donor coupled to a few-electron quantum dot in silicon , 2012, 1203.5264.

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

[29]  Denise Manahan-Vaughan,et al.  Hippocampal long-term depression: master or minion in declarative memory processes? , 2007, Trends in Neurosciences.

[30]  Denise Manahan-Vaughan,et al.  Hippocampal Long-Term Depression as a Declarative Memory Mechanism , 2005 .

[31]  Nathaniel B Sawtell,et al.  Neural mechanisms for filtering self-generated sensory signals in cerebellum-like circuits , 2011, Current Opinion in Neurobiology.

[32]  T. Hasegawa,et al.  Controlling the Synaptic Plasticity of a Cu2S Gap‐Type Atomic Switch , 2012 .

[33]  Daisuke Fujita,et al.  Multi-level memory-switching properties of a single brain microtubule , 2013 .

[34]  Tetsuya Asai,et al.  Neuronal synchrony detection on single-electron neural networks , 2006 .

[35]  M. Tsodyks,et al.  Synaptic Theory of Working Memory , 2008, Science.

[36]  B. Derrick,et al.  Long-Term Potentiation and Depression as Putative Mechanisms for Memory Formation , 2007 .

[37]  G. Lynch,et al.  Selective impairment of learning and blockade of long-term potentiation by an N-methyl-D-aspartate receptor antagonist, AP5 , 1986, Nature.

[38]  Wulfram Gerstner,et al.  Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns , 1993, Biological Cybernetics.

[39]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[40]  Henry C. Tuckwell,et al.  Weak Noise in Neurons May Powerfully Inhibit the Generation of Repetitive Spiking but Not Its Propagation , 2010, PLoS Comput. Biol..

[41]  J. White,et al.  Channel noise in neurons , 2000, Trends in Neurosciences.

[42]  L. Abbott,et al.  Redundancy Reduction and Sustained Firing with Stochastic Depressing Synapses , 2002, The Journal of Neuroscience.

[43]  F. Nori,et al.  Quantum biology , 2012, Nature Physics.

[44]  Eric R. Kandel,et al.  Transgenic Mice Lacking NMDAR-Dependent LTD Exhibit Deficits in Behavioral Flexibility , 2008, Neuron.

[45]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[46]  Zhiyong Li,et al.  Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions , 2010, Advanced materials.

[47]  Tetsuya Asai,et al.  Impact of Noise on Spike Transmission through Serially Connected Electrical FitzHugh-Nagumo Circuits with Subthreshold and Suprathreshold Interconductances , 2012 .