Analysis and optimization of the edge effect for III–V nanowire synthesis via selective area metal-organic chemical vapor deposition

Abstract

Selective Area Metal-Organic Chemical Vapor Deposition (SA-MOCVD) is a promising technique for the scale-up of nanowire fabrication. Our previous study investigated the growth mechanism of SA-MOCVD processes by quantifying contributions from various diffusion sources. However, the edge effect on nanostructure uniformity captured by skirt area diffusion was not quantitatively analyzed. This work further improves our understanding of the process by considering the edge effect as a superposition of skirt area diffusion and “blocking effect” and optimizing the edge effect for uniformity control of nanowire growth. We directly model the blocking effect of nanowires in the process of precursor diffusion from the skirt area to the center of a substrate. The improved model closely captures the distribution of the nanowire length across the substrate. Physical interpretation of the edge effect is provided. With the established model, we provide a method to optimize the width of the skirt area to improve the predicted structural uniformity of SA-MOCVD growth.

Keywords:

• Edge effect
• nanomanufacturing
• nanostructure uniformity control
• nanowire synthesis

Additional information

Notes on contributors

Yanqing Duanmu

Yanqing Duanmu received her B.S. degree in Physics in 2012 from the University of Science and Technology of China. She is currently a Ph.D. student in the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California. Her research focuses on modeling and analysis of complex systems, with special interest in the scale-up of nanomanufacturing.

Qiang Huang

Qiang Huang received his Ph.D. degree in Industrial and Operations Engineering in 2003 from the University of Michigan–Ann Arbor. He is currently an Associate Professor and Gordon S. Marshall Early Career Chair in Engineering in the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles. His research focuses on the modeling and analysis of complex systems for quality and productivity improvement, with special interest in integrated nanomanufacturing and nanoinformatics and additive manufacturing. He is a member of the IEEE, IIE, INFORMS, and ASME. He received the 2013 IEEE Transactions on Automation Science and Engineering Best Paper Award. He has been an Associate Editor of the IEEE Transactions on Automation Science and Engineering since 2012 and has been a member of the scientific committee (Editorial Board) for the North American Manufacturing Research Institution (NAMRI) of SME, 2009–2011 and 2013–2015.