Single-Electron Devices and Circuits Utilizing Stochastic Operation for Intelligent Information Processing

The single-electron circuit technology should aim at developing information processing systems using the intrinsic properties of single-electron devices. The operation principles of single-electron devices are completely different from that of conventional CMOS devices, but both devices should co-exist in the information processing systems. In this paper, according to a scenario for achieving large-scale integrated systems of single-electron devices, some single-electron devices and circuits utilizing stochastic operation for associative processing and a spiking neuron model are described. [Article copies are available for purchase from InfoSci-on-Demand.com]

[1]  Atsushi Iwata,et al.  A multi-nano-dot circuit and structure using thermal-noise-assisted tunneling for stochastic associative processing. , 2002, Journal of nanoscience and nanotechnology.

[2]  Wolfgang Maass,et al.  Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons , 1996, NIPS.

[3]  T. Fuyuki,et al.  Coulomb-staircase observed in silicon-nanodisk structures fabricated by low-energy chlorine neutral beams , 2007 .

[4]  Kaoru Nakano,et al.  Associatron-A Model of Associative Memory , 1972, IEEE Trans. Syst. Man Cybern..

[5]  Hiroshi Inokawa,et al.  Silicon single-electron devices , 1999 .

[6]  O. Saito,et al.  A 1M synapse self-learning digital neural network chip , 1998, 1998 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, ISSCC. First Edition (Cat. No.98CH36156).

[7]  Adi R. Bulsara,et al.  Tuning in to Noise , 1996 .

[8]  Sandip Tiwari,et al.  A silicon nanocrystals based memory , 1996 .

[9]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[10]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Takashi Morie,et al.  A multinanodot floating-gate MOSFET circuit for spiking neuron models , 2003 .

[12]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[13]  Atsushi Iwata,et al.  An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures , 2001, NIPS.

[14]  Ken Uchida,et al.  Novel Si Quantum Memory Structure with Self-Aligned Stacked Nanocrystalline Dots , 2000 .

[15]  Rachel Armstrong.  Unconventional Computing in the Built Environment , 2011, Int. J. Nanotechnol. Mol. Comput..

[16]  Hirofumi Shinohara,et al.  A 1.2GFLOPS neural network chip exhibiting fast convergence , 1994, Proceedings of IEEE International Solid-State Circuits Conference - ISSCC '94.

[17]  Koichi Ito,et al.  Toward Biomolecular Computers Using Reaction-Diffusion Dynamics , 2009, Int. J. Nanotechnol. Mol. Comput..

[18]  Takayuki Takahagi,et al.  Electrical properties of self-organized nanostructures of alkanethiol-encapsulated gold particles , 2000 .

[19]  Y. Hirai.  A PDM digital neural network system with 1000 neurons fully interconnected via 1000000 6-bit synapses , 1996 .

[20]  L.D. Jackel,et al.  An associative memory based on an electronic neural network architecture , 1987, IEEE Transactions on Electron Devices.

[21]  Katsunari Shibata,et al.  A self-learning digital neural network using wafer-scale LSI , 1993 .

[22]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[23]  Hideki Murakami,et al.  Memory Operation of Silicon Quantum-Dot Floating-Gate Metal-Oxide-Semiconductor Field-Effect Transistors , 2001 .

[24]  Atsushi Iwata,et al.  Quantum-dot structures measuring Hamming distance for associative memories , 2000 .

[25]  Vincenzo Manca,et al.  Algorithmic Models of Biochemical Dynamics: MP Grammars Synthetizing Complex Oscillators , 2011, Int. J. Nanotechnol. Mol. Comput..

[26]  Takashi Morie,et al.  An all-analog expandable neural network LSI with on-chip backpropagation learning , 1994, IEEE J. Solid State Circuits.

[27]  Atsushi Iwata,et al.  A single-electron stochastic associative processing circuit robust to random background-charge effects and its structure using nanocrystal floating-gate transistors , 2000 .