Imperial College London
Portrait Picture

Dr Christos Papavassiliou

Faculty of Engineering, Department of Electrical and Electronic Engineering

Reader in Instrumentation Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6325c.papavas Website

 
 
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Assistant

 

Mrs Wiesia Hsissen +44 (0)20 7594 6261

 
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Location

 

915Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Prodromakis:2023:10.1109/ISCAS46773.2023.10182200,
author = {Prodromakis, T and Papavassiliou, C and Si, Z and Serb, A and Huang, G and Wang, C},
doi = {10.1109/ISCAS46773.2023.10182200},
publisher = {IEEE},
title = {An improved data-driven memristor model accounting for sequences stimulus features},
url = {http://dx.doi.org/10.1109/ISCAS46773.2023.10182200},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The natural similarity between the emerging memristive technology and synapses makes memristor a promising device in the spiking input based neuromorphic systems. However, while asynchronous signal processing relies on memristor's response under the pulses stimulus, hardly any memristor models take the impact of sequences features on device behaviour into account. This paper proposes an optimized data-driven compact memristor model where the boundary of its internal state variable-resistive state (RS) is modelled with pulse amplitude and pulse width based on characterisation data. The model has been developed in Verilog-A and verified in Cadence Virtuoso Electronic Design Automation (EDA) tools. Based on the simulation, we further introduce a new concept “Effective Time Window”. Along with the observed pulse width modulated resistance, more potential circuit applications can be implemented based on a more realistic memristor switching behaviour.
AU  - Prodromakis,T
AU  - Papavassiliou,C
AU  - Si,Z
AU  - Serb,A
AU  - Huang,G
AU  - Wang,C
DO  - 10.1109/ISCAS46773.2023.10182200
PB  - IEEE
PY  - 2023///
SN  - 2158-1525
TI  - An improved data-driven memristor model accounting for sequences stimulus features
UR  - http://dx.doi.org/10.1109/ISCAS46773.2023.10182200
UR  - https://ieeexplore.ieee.org/document/10182200
UR  - http://hdl.handle.net/10044/1/105924
ER  -
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