Optimization of thin-film design for multi-layer dielectric grating

Thin-film design used to fabricate multi-layer dielectric (MLD) gratings should provide high transmittance during holography exposure, high reflectance at use wavelength and sufficient manufacturing latitude of the grating design making the MLD grating achieve both high diffraction efficiency and low electric field enhancement. Based on a (HLL)H-9 design comprising of quarter-waves of high-index material and half-waves of low-index material, we obtain an optimum MLD coating meeting these requirements by inserting a matching layer being half a quarter-wave of Al2O3 between the initial design and an optimized HfO2 top layer. The optimized MLD coatings exhibits a low reflectance of 0.017% under photoresist at the exposure angle of 17.8 degrees for 413 nm light and a high reflectance of 99.61% under air at the use angle of 51.2 degrees for 1053 nm light. Numerical calculation of intensity distribution in the photoresist coated on the MLD film during exposure shows that standing-wave patterns are greatly minimized and thus simulation profile of photoresist gratings after development demonstrates smoother shapes with lower roughness. Furthermore, a MLD gratings with grooves etched into the top layer of this MLD coating provides a high diffraction efficiency of 99.5% and a low electric field enhancement ratio of 1.53. This thin-film design shows perfect performances and can be easily fabricated by e-beam evaporation. (c) 2006 Elsevier B.V. All rights reserved.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.