Chemical field effect transistor response with post processing supervised neural network

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

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

    5 Citations (Scopus)

    Abstract

    This work presents the classification of potassium ion concentration in the presence of interfering ammonium ions from Chemical Field-Effect Transistor (CHEMFET) sensors involving neural network post-processing stage. Data collection for the purpose of supervised learning training data is obtained from sample solutions prepared by keeping the main ion concentration constant while the activity of the interfering ions based on the fixed interference method. The measurement setup includes a readout interface circuit that ensures constant-current constant-voltage across the drainsource for isothermal point operation. The training algorithm is back-propagation with generalized delta rule on a multilayer feed-forward network. Activation function based on the MOSFET drain current equation in the linear region is attempted in the hidden layer. Using function fitting approach, the network aims to find the potassium ion concentration despite the presence of interfering ion, without having to estimate device and chemically related parameters that would otherwise require further experiments.

    Original languageEnglish
    Title of host publicationSoCPaR 2009 - Soft Computing and Pattern Recognition
    Pages250-253
    Number of pages4
    DOIs
    Publication statusPublished - 2009
    EventInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 - Malacca
    Duration: 4 Dec 20097 Dec 2009

    Other

    OtherInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009
    CityMalacca
    Period4/12/097/12/09

    Fingerprint

    Field effect transistors
    Neural networks
    Ions
    Processing
    Potassium
    Drain current
    Supervised learning
    Backpropagation
    Multilayers
    Chemical activation
    Networks (circuits)
    Sensors
    Electric potential
    Experiments

    Keywords

    • Back-propagation
    • Chemical sensor
    • FIM
    • Readout circuit
    • Semiconductor device

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Vision and Pattern Recognition
    • Software

    Cite this

    Abdullah, W. F. H., Othman, M., Berhad, M., & Ali, M. A. M. (2009). Chemical field effect transistor response with post processing supervised neural network. In SoCPaR 2009 - Soft Computing and Pattern Recognition (pp. 250-253). [5370318] https://doi.org/10.1109/SoCPaR.2009.58

    Chemical field effect transistor response with post processing supervised neural network. / Abdullah, Wan Fazlida Hanim; Othman, Masuri; Berhad, Mimos; Ali, Mohd Alaudin Mohd.

    SoCPaR 2009 - Soft Computing and Pattern Recognition. 2009. p. 250-253 5370318.

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

    Abdullah, WFH, Othman, M, Berhad, M & Ali, MAM 2009, Chemical field effect transistor response with post processing supervised neural network. in SoCPaR 2009 - Soft Computing and Pattern Recognition., 5370318, pp. 250-253, International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009, Malacca, 4/12/09. https://doi.org/10.1109/SoCPaR.2009.58
    Abdullah WFH, Othman M, Berhad M, Ali MAM. Chemical field effect transistor response with post processing supervised neural network. In SoCPaR 2009 - Soft Computing and Pattern Recognition. 2009. p. 250-253. 5370318 https://doi.org/10.1109/SoCPaR.2009.58
    Abdullah, Wan Fazlida Hanim ; Othman, Masuri ; Berhad, Mimos ; Ali, Mohd Alaudin Mohd. / Chemical field effect transistor response with post processing supervised neural network. SoCPaR 2009 - Soft Computing and Pattern Recognition. 2009. pp. 250-253
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