DocumentCode
3601290
Title
On the Non-STDP Behavior and Its Remedy in a Floating-Gate Synapse
Author
Gopalakrishnan, Roshan ; Basu, Arindam
Author_Institution
IC Design Centre of Excellence, Nanyang Technol. Univ., Singapore, Singapore
Volume
26
Issue
10
fYear
2015
Firstpage
2596
Lastpage
2601
Abstract
This brief describes the neuromorphic very large scale integration implementation of a synapse utilizing a single floating-gate (FG) transistor that can be used to store a weight in a nonvolatile manner and demonstrate biological learning rules such as spike-timing-dependent plasticity (STDP). The experimental STDP plot (change in weight against △t = tpost - tpre) of a traditional FG synapse from previous studies shows a depression instead of potentiation at some range of positive values of △t-we call this non-STDP behavior. In this brief, we first analyze theoretically the reason for this anomaly and then present a simple solution based on changing control gate waveforms of the FG device to make the weight change conform closely to biological observations over a wide range of parameters. The experimental results from an FG synapse fabricated in AMS 0.35-μm CMOS process design are also presented to justify the claim. Finally, we present the simulation results of a circuit designed to create the modified gate voltage waveform.
Keywords
CMOS integrated circuits; VLSI; transistor circuits; AMS CMOS process; biological learning rules; biological observations; changing control gate waveforms; floating gate synapse; modified gate voltage waveform; neuromorphic very large scale integration; nonSTDP behavior; single floating-gate transistor; size 0.35 mum; spike-timing-dependent plasticity; Equations; Logic gates; Mathematical model; Simulation; Transistors; Tunneling; Very large scale integration; Floating gate (FG); learning; neuromorphic; neuroscience; spike-timing-dependent plasticity (STDP); synapse; very large scale integration (VLSI); very large scale integration (VLSI).;
fLanguage
English
Journal_Title
Neural Networks and Learning Systems, IEEE Transactions on
Publisher
ieee
ISSN
2162-237X
Type
jour
DOI
10.1109/TNNLS.2015.2388633
Filename
7031963
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