Research Papers

Optimization of Gallium Nitride Metalorganic Chemical Vapor Deposition Process

[+] Author and Article Information
Pradeep George

Division of Engineering,
New York University Abu Dhabi,
P.O. Box 129188,
Abu Dhabi, UAE
e-mail: pg66@nyu.edu

Jiandong Meng

Department of Mechanical
and Aerospace Engineering,
Rutgers, The State University of New Jersey,
Piscataway, NJ 08854

Yogesh Jaluria

Department of Mechanical
and Aerospace Engineering,
Rutgers, The State University of New Jersey,
Piscataway, NJ 08854

1Corresponding author.

Manuscript received April 4, 2014; final manuscript received May 31, 2014; published online March 17, 2015. Assoc. Editor: Giulio Lorenzini.

J. Heat Transfer 137(6), 061007 (Jun 01, 2015) (8 pages) Paper No: HT-14-1171; doi: 10.1115/1.4029859 History: Received April 04, 2014; Revised May 31, 2014; Online March 17, 2015

This paper investigates the simulation, response surface modeling, and optimization of the metalorganic chemical vapor deposition (MOCVD) process for the deposition of gallium nitride (GaN). Trimethylgallium (TMGa) and ammonia (NH3) are the precursors carried by hydrogen into the rotating-disk reactor. The deposition rate of GaN film and its uniformity form the focus of this study. Computational fluid dynamics (CFD) model simulates the deposition of the GaN film. CFD model is employed to identify two design variables, inlet velocity and inlet precursor concentration ratio, which significantly affect the deposition rate and uniformity of GaN film. Compromise response surface method (CRSM) is used to generate response surfaces for average deposition rate and uniformity. These response surfaces are used to generate the Pareto front for the conflicting objectives of optimal rate of average deposition and uniformity. Pareto front captures the trade-off between deposition rate and uniformity of the GaN film. It is observed that for the whole range of design variables, there are numerous options to get stable uniformity levels than deposition rate. The optimal inlet velocity and precursor concentration for different objective functions considered tend to be near the upper bounds.

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Fig. 1

Cross section of reactor geometry [47]

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Fig. 2

Effect of Vin on average deposition rate

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Fig. 3

Deposition rate along the radius for different Vin

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Fig. 4

Effect of Cin on average deposition rate

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Fig. 5

Deposition rate along the radius for different Cin

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Fig. 6

Schematic of the deposition profile over the wafer

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Fig. 12

Pareto front for equal weight optimization problem in smaller domain

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Fig. 11

Pareto frontiers in Fig. 10 represented in the design space

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Fig. 10

Pareto front for optimization problem in Eqs. (10) and (11)

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Fig. 9

Response surface: uniformity parameter, UP90

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Fig. 8

Response surface: uniformity parameter, UP95

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Fig. 7

Response surface: average deposition rate




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