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
Link To Document :
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