DocumentCode :
777607
Title :
A multinanodot floating-gate MOSFET circuit for spiking neuron models
Author :
Morie, Takashi ; Matsuura, Tomohiro ; Nagata, Makoto ; Iwata, Atsushi
Author_Institution :
Graduate Sch. of Life Sci. & Syst. Eng., Kyushu Inst. of Technol., Kitakyushu, Japan
Volume :
2
Issue :
3
fYear :
2003
Firstpage :
158
Lastpage :
164
Abstract :
Spiking neuron models, which represent information in the form of spatiotemporal patterns in spike pulse trains, have attracted much attention recently in the fields of computational neuroscience and artificial neural networks. The information processing abilities of spiking neuron models have been proven superior to those of the conventional analog-type (rate-coding) neural network models. In particular, the spike response model (SRM), which simplifies the biological neuron operation from the viewpoint of spike response, is important for VLSI implementation and various applications. In the SRM, the generation of post-synaptic potentials (PSPs) is essential. The conventional CMOS devices require complicated circuits in order to realize the function of SRM neurons. In this paper, a new device structure using a MOSFET with multinanodot floating-gate arrays is proposed for the synapse component of SRM neurons. This structure can operate at room temperature, as it utilizes thermal-noise-assisted tunneling between nanodots. The structure generates PSPs by taking advantage of the delay in electron movement due to stochastic tunneling processes. The results of single-electron circuit simulation demonstrate the generation of PSPs. The proposed structure has not yet been fabricated. The aim of this paper is to propose guidelines for the development of new nanoscale devices and fabrication technology for intelligent information processing such as that achieved in the human brain.
Keywords :
MOSFET circuits; Monte Carlo methods; circuit simulation; nanoelectronics; neural chips; semiconductor quantum dots; single electron transistors; tunnelling; Monte Carlo single-electron simulator; SRM; VLSI implementation; analog type models; artificial neural networks; associative memory circuits; electron movement delay; high rate power dissipation; intelligent brain-like information processing; intelligent information processing; internal potential; knowledge; multinanodot floating-gate MOSFET circuit; nanoscale devices; nanotechnology; post-synaptic potentials; rate-coding neural network models; recognition; single-electron circuit simulation; spatiotemporal patterns; spike response model; spiking neuron models; stochastic tunneling processes; thermal-noise-assisted tunneling; total energy profile; weighting; Artificial neural networks; Biological system modeling; Biology computing; Computer networks; Information processing; MOSFET circuits; Neurons; Semiconductor device modeling; Spatiotemporal phenomena; Tunneling;
fLanguage :
English
Journal_Title :
Nanotechnology, IEEE Transactions on
Publisher :
ieee
ISSN :
1536-125X
Type :
jour
DOI :
10.1109/TNANO.2003.817221
Filename :
1230117
Link To Document :
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