We succeeded in measuring the density and direction of the edge component of threading dislocations (TDs) in c-plane (0001) GaN by micro-Raman spectroscopy mapping. In the micro-Raman spectroscopy mapping of the E2H peak shift between 567.85 and 567.75 cm−1, six different contrast images are observed toward directions of . By comparing X-ray topography and etch pit images, the E2H peak shift is observed where the edge component of TDs exists. In contrast, the E2H peak is not observed where the screw component of TDs exists.

Accelerating the optimization of material processing is essential for rapid prototyping of advanced materials to achieve practical applications. High-quality and large-diameter semiconductor crystals improve the performance, reliability and cost efficiency of semiconductor devices. However, much time is required to optimize the growth conditions and obtain a superior semiconductor crystal. Here, we demonstrate a rapid prediction of the results of computational fluid dynamics (CFD) simulations for SiC solution growth using a neural network for optimization of the growth conditions. The prediction speed was 10 7 times faster than that of a single CFD simulation. The combination of the CFD simulation and machine learning thus makes it possible to determine optimized parameters for high-quality and large-diameter crystals. Such a simulation is therefore expected to become the technology employed for the design and control of crystal growth processes. The method proposed in this study will also be useful for simulations of other processes.6546 | CrystEngComm, 2018, 20, 6546-6550This journal is

In the design of a crystal growth system, the ability to efficiently regulate intertwined geometrical parameters is crucial for its successful development and commercialization. However, the traditional experimental and computational...

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