Abstract
Abstract Recently a surface plasmon resonance (SPR) optical sensor, based on the Otto configuration — the Otto chip — has been developed. One essential step in the quality control of the fabrication process is characterization of the active region of several devices in a batch. Characterization is done by measuring the angular spectrum of the optical re ectance on several points across the active region of the device, and determining parameters by regression analysis of the data. Traditional gradient methods used in the regression process are extremely dependent on an initial guess and are not very efficient for batch analysis of curves, when those include poorly defined SPR spectra, where an initial guess may be hard to infer. An alternative approach for the regression problem is to model the analysis as an optimization problem and using an efficient stochastic algorithm. In this paper one discusses the use of Particle Swarm Optimization (PSO) for characterization of Otto chip devices. From comparative studies carried out in an existing Otto chip, it is observed that PSO can be a very efficient approach for batch analysis and yields better results when compared with the traditional gradient-based regression method.
Highlights
Surface Plasmon Resonance (SPR) [1] can be observed in the optical reflectance function of a metal surface, in a specially configured multilayer configuration
In this work we proposed the use of the Particle Swarm Optimization algorithm for the characterization of Otto chip devices
The strategy was to adapt the principle, which is generally used in optimization problems, to perform regression analysis of data
Summary
Surface Plasmon Resonance (SPR) [1] can be observed in the optical reflectance function of a metal surface, in a specially configured multilayer configuration. An alternative approach for the regression problem is to model the analysis as an optimization problem and using an efficient optimization algorithm This alternative has already been applied on the extraction of optical constants for Kretschmann’s configuration [14]–[17]. The focus of the present paper is to apply a similar approach for the nonlinear regression analysis of Otto chip devices [19], and to determine to what extent this strategy can be efficient and amenable to large scale device characterization once, as further explained, nonlinear regression analysis on Otto’s configuration have to deal with two sets of parameters as possible solutions. Unlike the gradient-based approach, PSO does not require determining an initial estimate of parameters and can be used to automate the regression for a large number of data sets, as demonstrated
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