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Journal of Biomedicine and Biotechnology
Volume 2008 (2008), Article ID 259174, 10 pages
Research Article

Bio-Inspired Microsystem for Robust Genetic Assay Recognition

1Department of Electrical Engineering - Electrophysics, University of Southern California, Los Angeles, CA 90089, USA
2Department of Electronics Egineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, China

Received 14 January 2008; Accepted 25 March 2008

Academic Editor: Daniel Howard

Copyright © 2008 Jaw-Chyng Lue and Wai-Chi Fang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low-intensity patterns. Our results show that the trained new ANN can recognize low-fluorescence patterns better than an ANN using the conventional sigmoid function.