Bio-Inspired Microsystem for Robust Genetic Assay Recognition

Abstract EN

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.