Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification

Abstract

The casting mono-like silicon (Si) grown by directional solidification (DS) is promising for high-efficiency solar cells. However, high dislocation clusters around the top region are still the practical drawbacks, which limit its competitiveness to the monocrystalline Si. To optimize the DS-Si process, we applied the framework, which integrates the growing experiments, transient global simulations, artificial neuron network (ANN) training, and genetic algorithms (GAs). First, we grew the Si ingot by the original recipe and reproduced it with transient global modeling. Second, predictions of the Si ingot domain from different recipes were used to train the ANN, which acts as the instant predictor of ingot properties from specific recipes. Finally, the GA equipped with the predictor searched for the optimal recipe according to multi-objective combination, such as the lowest residual stress and dislocation density. We also implemented the optimal recipe in our mono-like DS-Si process for verification and comparison. According to the optimal recipe, we could reduce the dislocation density and smooth the growth rate during the Si ingot growing process. Comparisons of the growth interface and grain boundary evolutions showed the decrease of the interface concavity and the multi-crystallization in the top part of the ingot. The well-trained ANN combined with the GA could derive the optimal growth parameter combinations instantly and quantitatively for the multi-objective processes.

Analysis of grain growth behavior of multicrystalline Mg2Si

“…10. 32,33) Figure 11 shows the crystallization rate obtained from the crystal growth simulation, which is calculated as.…”

Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation

“…In this paper, we report on the integration of experimental, theoretical, computational, and data sciences in multiscale cyber and physical spaces, including research foundations developed in the project such as 3D visualization of dislocation clusters in mc‐Si ingot, [ 24 ] prediction of crystal orientation from optical images using machine learning models, [ 40,41 ] crystal growth simulation, [ 42 ] finite element analysis considering anisotropy of elastic constants, observation using electron microscopes, and ab initio calculations, and so on. Integration of these analytical methods made us construct a realistic 3D model of mc‐Si and study phenomena that have been difficult to approach from new perspectives, such as dislocation generation in complex multicrystalline materials.…”

Temperature Field, Flow Field, and Temporal Fluctuations Thereof in Ammonothermal Growth of Bulk GaN—Transition from Dissolution Stage to Growth Stage Conditions

“…In SiC solution growth, for instance, the tremendously accelerated prediction of growth conditions has been demonstrated [14]. In the directional solidification of silicon, the accelerated optimization of growth conditions has been presented [15]. Therefore, once the physics have been clarified and sufficiently accurate models have been developed on that basis, machine learning from numerical simulation data may provide a pathway to realize computationally efficient compact models for ammonothermal crystal growth.…”