Ion-selective field-effect transistor sensor response for neural network supervised learning

Wan Fazlida Hanim Abdullah, Masuri Othman, Mohd Alaudin Mohd Ali

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Ion-selective field transistors (ISFETs) are electrochemical sensors that can detect ion activities but have low selectivity issues for mixed-ion environments. This paper presents the data extraction and investigation of K+ ISFET sensors for neural network as post-processing stage. The environment for the sensor application is potassium and calcium mixed ions that is represented by solutions prepared based on the fixed interference method. The device measurement approach used is MOSFET semiconductor characterization technique, with further extracted data from the transfer characteristics subjected to statistical analysis. Results show that interfering ions influence the sensitivity graph of sensors detecting the main ion by shifting the gradient by 17%. Mean value of voltage response across the interfering ion range results in shifts up to 60 mV. Analysis of variance test gives a small pvalue indicating noticeable mean value of voltage response relating to change of main ion activity despite a large error variance possibly from the interfering ionic activity purposely added to the solutions. Extracted data from the solutions is then subjected to neural network pattern recognition by supervised learning method giving 73.7% correct recognition.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Semiconductor Electronics, Proceedings, ICSE
    Pages126-129
    Number of pages4
    DOIs
    Publication statusPublished - 2008
    Event2008 IEEE International Conference on Semiconductor Electronics, ICSE 2008 - Johor Bahru, Johor
    Duration: 25 Nov 200827 Nov 2008

    Other

    Other2008 IEEE International Conference on Semiconductor Electronics, ICSE 2008
    CityJohor Bahru, Johor
    Period25/11/0827/11/08

    Fingerprint

    Supervised learning
    Field effect transistors
    Ions
    Neural networks
    Sensors
    Transistors
    Electrochemical sensors
    Electric potential
    Analysis of variance (ANOVA)
    Pattern recognition
    Potassium
    Calcium
    Statistical methods
    Semiconductor materials
    Processing

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Electronic, Optical and Magnetic Materials

    Cite this

    Abdullah, W. F. H., Othman, M., & Ali, M. A. M. (2008). Ion-selective field-effect transistor sensor response for neural network supervised learning. In IEEE International Conference on Semiconductor Electronics, Proceedings, ICSE (pp. 126-129). [4770291] https://doi.org/10.1109/SMELEC.2008.4770291

    Ion-selective field-effect transistor sensor response for neural network supervised learning. / Abdullah, Wan Fazlida Hanim; Othman, Masuri; Ali, Mohd Alaudin Mohd.

    IEEE International Conference on Semiconductor Electronics, Proceedings, ICSE. 2008. p. 126-129 4770291.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abdullah, WFH, Othman, M & Ali, MAM 2008, Ion-selective field-effect transistor sensor response for neural network supervised learning. in IEEE International Conference on Semiconductor Electronics, Proceedings, ICSE., 4770291, pp. 126-129, 2008 IEEE International Conference on Semiconductor Electronics, ICSE 2008, Johor Bahru, Johor, 25/11/08. https://doi.org/10.1109/SMELEC.2008.4770291
    Abdullah WFH, Othman M, Ali MAM. Ion-selective field-effect transistor sensor response for neural network supervised learning. In IEEE International Conference on Semiconductor Electronics, Proceedings, ICSE. 2008. p. 126-129. 4770291 https://doi.org/10.1109/SMELEC.2008.4770291
    Abdullah, Wan Fazlida Hanim ; Othman, Masuri ; Ali, Mohd Alaudin Mohd. / Ion-selective field-effect transistor sensor response for neural network supervised learning. IEEE International Conference on Semiconductor Electronics, Proceedings, ICSE. 2008. pp. 126-129
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