担当区分：筆頭著者,　責任著者   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier BV  

DOI： 10.1016/j.jcrysgro.2021.126190

記述言語：英語   出版者・発行元：Crystals  

Numerical simulations are a valuable tool for the design and optimization of crystal growth processes because experimental investigations are expensive and access to internal parameters is limited. These technical limitations are particularly large for ammonothermal growth of bulk GaN, an important semiconductor material. This review presents an overview of the literature on simulations targeting ammonothermal growth of GaN. Approaches for validation are also reviewed, and an overview of available methods and data is given. Fluid flow is likely in the transitional range between laminar and turbulent; however, the time-averaged flow patterns likely tend to be stable. Thermal boundary conditions both in experimental and numerical research deserve more detailed evaluation, especially when designing numerical or physical models of the ammonothermal growth system. A key source of uncertainty for calculations is fluid properties under the specific conditions. This originates from their importance not only in numerical simulations but also in designing similar physical model systems and in guiding the selection of the flow model. Due to the various sources of uncertainty, a closer integration of numerical modeling, physical modeling, and the use of measurements under ammonothermal process conditions appear to be necessary for developing numerical models of defined accuracy.

DOI： 10.3390/cryst11040356

Scopus

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Crystals  

Thermal boundary conditions for numerical simulations of ammonothermal GaN crystal growth are investigated. A global heat transfer model that includes the furnace and its surroundings is presented, in which fluid flow and thermal field are treated as conjugate in order to fully account for convective heat transfer. The effects of laminar and turbulent flow are analyzed, as well as those of typically simultaneously present solids inside the autoclave (nutrient, baffle, and multiple seeds). This model uses heater powers as a boundary condition. Machine learning is applied to efficiently determine the power boundary conditions needed to obtain set temperatures at specified locations. Typical thermal losses are analyzed regarding their effects on the temperature distribution inside the autoclave and within the autoclave walls. This is of relevance because autoclave wall temperatures are a convenient choice for setting boundary conditions for simulations of reduced domain size. Based on the determined outer wall temperature distribution, a simplified model containing only the autoclave is also presented. The results are compared to those observed using heater-long fixed temperatures as boundary condition. Significant deviations are found especially in the upper zone of the autoclave due to the important role of heat losses through the autoclave head.

DOI： 10.3390/cryst11030254

Scopus

担当区分：筆頭著者,　責任著者  

担当区分：筆頭著者,　責任著者  

開催年月日： 2020年3月