The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Elias A. Flores - One of the best experts on this subject based on the ideXlab platform.
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Biological plausibility and stochasticity in scalable VO2 active memristor neurons
Nature Communications, 2018Co-Authors: Wei Yi, Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.The neuromorphic computing based on complementary metal-oxide-semiconductor Transistors holds promise for artificial intelligence, but it suffers from the trade-off between scalability and biological fidelity. Yi et al. emulate 23 types of biological neuronal behaviors using scalable VO2 active memristors.
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biological plausibility and stochasticity in scalable vo 2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
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biological plausibility and stochasticity in scalable vo2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
John A Rogers - One of the best experts on this subject based on the ideXlab platform.
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high speed mechanically flexible single crystal Silicon thin film Transistors on plastic substrates
IEEE Electron Device Letters, 2006Co-Authors: Jong Hyun Ahn, Etienne Menard, Hoonsik Kim, Keon Jae Lee, Zhengtao Zhu, Ralph G Nuzzo, John A RogersAbstract:This letter describes the fabrication and properties of bendable single-crystal-Silicon thin film Transistors formed on plastic substrates. These devices use ultrathin single-crystal Silicon ribbons for the semiconductor, with optimized device layouts and low-temperature gate dielectrics. The level of performance that can be achieved approaches that of traditional Silicon Transistors on rigid bulk wafers: effective mobilities>500cm/sup 2//V/spl middot/s, ON/OFF ratios >10/sup 5/, and response frequencies > 500 MHz at channel lengths of 2 /spl mu/m. This type of device might provide a promising route to flexible digital circuits for classes of applications whose performance requirements cannot be satisfied with organic semiconductors, amorphous Silicon, or other related approaches.
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bendable single crystal Silicon thin film Transistors formed by printing on plastic substrates
Applied Physics Letters, 2005Co-Authors: Etienne Menard, Ralph G Nuzzo, John A RogersAbstract:Bendable, high performance single crystal Silicon Transistors have been formed on plastic substrates using an efficient dry transfer printing technique. In these devices, free standing single Silicon objects, which we refer to as microstructured Silicon (μs‐Si), are picked up, using a conformable rubber stamp, from the top surface of a wafer from which they are generated. The μs‐Si is then transferred, to a specific location and with a controlled orientation, onto a thin plastic sheet. The efficiency of this method is demonstrated by the fabrication of an array of thin film Transistors that exhibit excellent electrical properties: average device effective mobilities, evaluated in the linear regime, of ∼240cm2∕Vs, and threshold voltages near 0V. Frontward and backward bending tests demonstrate the mechanical robustness and flexibility of the devices.
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contact resistance in organic Transistors that use source and drain electrodes formed by soft contact lamination
Journal of Applied Physics, 2003Co-Authors: Jana Zaumseil, K W Baldwin, John A RogersAbstract:Soft contact lamination of source/drain electrodes supported by gold-coated high-resolution rubber stamps against organic semiconductor films can yield high-performance organic Transistors. This article presents a detailed study of the electrical properties of these devices, with an emphasis on the nature of the laminated contacts with the p- and n-type semiconductors pentacene and copper hexadecafluorophthalocyanine, respectively. The analysis uses models developed for characterizing amorphous Silicon Transistors. The results demonstrate that the parasitic resistances related to the laminated contacts and their coupling to the transistor channel are considerably lower than those associated with conventional contacts formed by evaporation of gold electrodes directly on top of the organic semiconductors. These and other attractive features of Transistors built by soft contact lamination suggest that they may be important for basic and applied studies in plastic electronics and nanoelectronic systems based ...
Kenneth K. Tsang - One of the best experts on this subject based on the ideXlab platform.
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Biological plausibility and stochasticity in scalable VO2 active memristor neurons
Nature Communications, 2018Co-Authors: Wei Yi, Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.The neuromorphic computing based on complementary metal-oxide-semiconductor Transistors holds promise for artificial intelligence, but it suffers from the trade-off between scalability and biological fidelity. Yi et al. emulate 23 types of biological neuronal behaviors using scalable VO2 active memristors.
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biological plausibility and stochasticity in scalable vo 2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
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biological plausibility and stochasticity in scalable vo2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
Stephen K. Lam - One of the best experts on this subject based on the ideXlab platform.
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Biological plausibility and stochasticity in scalable VO2 active memristor neurons
Nature Communications, 2018Co-Authors: Wei Yi, Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.The neuromorphic computing based on complementary metal-oxide-semiconductor Transistors holds promise for artificial intelligence, but it suffers from the trade-off between scalability and biological fidelity. Yi et al. emulate 23 types of biological neuronal behaviors using scalable VO2 active memristors.
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biological plausibility and stochasticity in scalable vo 2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
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biological plausibility and stochasticity in scalable vo2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
Jenna A.e. Crowell - One of the best experts on this subject based on the ideXlab platform.
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Biological plausibility and stochasticity in scalable VO2 active memristor neurons
Nature Communications, 2018Co-Authors: Wei Yi, Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.The neuromorphic computing based on complementary metal-oxide-semiconductor Transistors holds promise for artificial intelligence, but it suffers from the trade-off between scalability and biological fidelity. Yi et al. emulate 23 types of biological neuronal behaviors using scalable VO2 active memristors.
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biological plausibility and stochasticity in scalable vo 2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
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biological plausibility and stochasticity in scalable vo2 active memristor neurons
Nature Communications, 2018Co-Authors: Kenneth K. Tsang, Stephen K. Lam, Xiwei Bai, Jenna A.e. Crowell, Elias A. FloresAbstract:Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, Silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for Silicon processors may not be overcome by Moore’s law scaling of Silicon Transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.