Analysis of Molecular Beam Epitaxy Process for Growing Nanoscale Magnesium Oxide Films

[+] Author and Article Information
Ghulam M. Uddin, Zhuhua Cai, Katherine S. Ziemer, Abe Zeid

 Northeastern University, Boston, MA 02115

Sagar Kamarthi1

 Northeastern University, Boston, MA 02115sagar@coe.neu.edu


Corresponding author.

J. Manuf. Sci. Eng 132(3), 030913 (Jun 09, 2010) (9 pages) doi:10.1115/1.4001691 History: Received February 02, 2010; Revised April 20, 2010; Published June 09, 2010; Online June 09, 2010

Like most nanomanufacturing processes, molecular beam epitaxy (MBE) processes are based on atomic-level control of growing films and thus are sensitive to subtle changes that make repeatability and reproducibility of desired performance indicators a nontrivial task. The gamut of challenges include insufficient understanding of atomic-level interactions, involvement of a large number of candidate process variables, lack of direct observation and measurement techniques for key performance indicators, and significant cost and time requirements for conducting experiments. A conventional design of experiment-based analysis becomes an unrealistic option due to its demand on extensive experimentation. In this paper, we present a hybrid approach that combines current process knowledge, artificial neural networks, and design of experiments (DOE) to make use of preliminary experimental data to analyze the process behavior, enhance process knowledge, and lay down foundations for cost effective systematic experimentation. Based on preliminary experimental data generated while exploring the MBE process for growing a MgO interface layer on 6H-SiC substrate, we developed a neural-network-based meta model that can interpolate and estimate the process responses to any combination of process variable settings within the input space. Using the neural-network model trained on preliminary experimental data, we estimate the process responses for a three-level full-factorial DOE runs. Based on these runs, the DOE based analysis is carried out. The results help explain the MgO film growth dynamics with respect to process variables such as substrate temperature, growth time, magnesium source temperature, and trace oxygen on the initial substrate surface. This approach can be expanded to statistically analyze the dynamics of other complex nanoprocesses when only the exploratory preliminary experimental data are available. This approach can also lay the foundation for efficient and systematic experimentation to further analyze and optimize the processes to address issues such as process repeatability and reliability.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Schematic of effective integration of a functional oxide film with a semiconductor substrate using a simple oxide interlayer. Thickness of an effective MgO interlayer ranges from 2 nm to 10 nm depending on the application. This challenging first step in multifunctional heterostructure processing is critical to overall final device performance.

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Figure 2

Schematic of the MBE equipment being used in the experimentation

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Figure 3

Schematic of the exploratory cycles guiding experimentation

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Figure 4

Input-process-output (IPO) diagram for MgO film growth by MBE process

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Figure 5

Architecture of the MLPs used as the process meta model

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Figure 6

Error correction plot for O–Mg/O

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Figure 7

Error correction plot for OH–Mg/O

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Figure 8

Scatter plots for neural network estimated versus actual measurements for 107 sample

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Figure 9

Marginal mean plots for (a) O–Mg/O and (b) OH–Mg/O; A=growth time, B=substrate temperature (°C), C=magnesium source temperature (°C), and D=percentage oxygen on the starting surface

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Figure 10

Pareto charts for regression coefficients (a) O–Mg/O and (b) OH–Mg/O

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Figure 11

Response surface plots of (a) O–Mg/O and (b) OH–Mg/O as a function of oxygen percentage of hydrogen cleaned SiC surface and substrate temperature (growth time of 10 min and magnesium temperature of 340°C); response surface plots of (c) O–Mg/O and (d) OH–Mg/O as a function of oxygen percentage of hydrogen cleaned SiC surface and magnesium source temperature (growth time of 10 min and substrate temperature of 150°C)



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