Combinatorial Studies for High Density Si and Ge Nanoparticle Arrays
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0933-G05-11
Combinatorial Studies for High Density Si and Ge Nanoparticle Arrays Scott K. Stanley, and John G. Ekerdt Chemical Engineering, University of Texas at Austin, 1 University Station, C0400, Austin, TX, 78712
ABSTRACT A simple combinatorial approach for studying chemical and physical vapor deposition (CVD and PVD) nanoparticle growth is presented utilizing temperature and precursor flux gradients across sample surfaces. Large temperature gradients (450-700 oC) are induced covering the entire range of interest for most CVD and PVD processes. Precursor flux gradients may also be introduced simultaneously or separately using a tungsten cracking filament mounted on a translation arm. Using a point source model, calibration experiments are explained and results from a study on Ge nanoparticle growth on HfO2 surfaces are presented and analyzed. This method drastically decreases experimental time required to investigate nanoparticle growth and identify optimum deposition conditions. Furthermore, this approach greatly facilitates preparation of library samples containing a wide range (several orders of magnitude) in variation of nanoparticle sizes, density, and composition for subsequent studies. INTRODUCTION Optimizing chemical and physical vapor deposition (CVD and PVD) processes to realize a particular film or nanostructure composition with desired properties (such as thickness, particle size distribution, bandgap, etc.) is an arduous task. The parameter space for CVD processes is vast due to the large number of parameters to be investigated, such as growth temperature, precursor material and flux, substrate material and surface treatment, and reaction method (thermal, plasma, catalytic). Therefore, combinatorial studies could have a large impact on the efficiency of defining a process and identifying parameter regions of interest for detailed study. We show that combinatorial methods can be particularly useful in nanoparticle growth studies. This paper is divided into two parts. First, the simple design of a combinatorial CVD setup is presented. Methods to controllably generate gradients in composition, growth temperature, and precursor flux all on one surface are discussed and data from calibration experiments are analyzed and compared with predictions. Second, a combinatorial study is presented to identify parameters leading to high density nanoparticle arrays. Germanium nanoparticle growth on HfO2 is chosen due to the relevance to non-volatile memory applications (i.e. nanocrystal-based flash memory) [1]. The approach presented here decreases experimental time by orders of magnitude for examining nanoparticle growth and allows an enormous parameter space to be investigated in a single experiment. We are able to extract the flux and temperatures from these studies to “dial in” a desired nanoparticle size and coverage. Finally, optimum conditions are identified from the combinatorial study to create nanoparticle arrays.
Figure 1. Illustration of the UHV CVD system used for producing combinatorial samples.
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