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Author Abdullah, W.F.H. ♦ Othman, M. ♦ Ali, M.A.M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Physics ♦ Electricity & electronics ♦ Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword FETs ♦ Neural networks ♦ Supervised learning ♦ Data mining ♦ Sensor phenomena and characterization ♦ Voltage ♦ Pattern recognition ♦ MOSFETs ♦ Calcium ♦ Interference
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 -value 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.
Description Author affiliation: IMEN, Univ. Kebangsaan Malaysia, Bangi (Abdullah, W.F.H.; Ali, M.A.M.) || MIMOS Berhad, Taman Teknol. Malaysia, Kuala Lumpur (Othman, M.)
ISBN 9781424438730
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-11-25
Publisher Place Malaysia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 3.37 MB
Page Count 4
Starting Page 126
Ending Page 129

Source: IEEE Xplore Digital Library