Abstract

Cerium oxide-based slurries are crucial for chemical mechanical polishing (CMP) in electronic industry. For these slurry systems, poly(acrylic acid) (PAA) is heavily utilized to provide colloidal stability. Some of the important parameters in the colloid stability are molecular weight (MW) and concentration of stabilizer, size of the nanoparticle in the slurry and the pH of system. By determining the colloidal stability of a discrete number of slurry formulations and relating these to certain slurry component parameters, a possible model can be produced to predict the influence of these parameters on the particle stability. Direct quantification of colloidal stability is difficult, however, polymer adsorption has been well established to correlate with the stability and therefore it can be used to quantify the colloidal stability. For the current thesis, surface area of cerium oxide, molecular weight of PAA, and the relative weight fraction of PAA were varied in two different nanomaterial systems, such as nanocubes and nanorods. To obtain the best fit of these variables, as they relate to polymer adsorption, fittings were performed using two advanced modeling techniques; namely, artificial neural network and adaptive neuro-fuzzy inference system. The precision of these techniques were compared each other and with the more simple, though largely imprecise, multi-variable linear regression. It was determined that the GENFIS-3 model shows the best performance for describing polymer adsorption on the nanocube and nanorod systems with an average relative deviation of only 6.5%. Additionally, these models suggest that the relative fraction of PAA has the most significant effect on the stability of cerium oxide-based CMP slurries. The greater precision of these advanced modeling methods can explain the better slurry performance with greater colloidal stability.

Notes

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Graduation Date

2015

Semester

Fall

Advisor

Seal, Sudipta

Degree

Master of Science in Materials Science and Engineering (M.S.M.S.E.)

College

College of Engineering and Computer Science

Department

Materials Science Engineering

Degree Program

Materials Science and Engineering

Format

application/pdf

Identifier

CFE0006315

URL

http://purl.fcla.edu/fcla/etd/CFE0006315

Language

English

Release Date

June 2016

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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