Abstract: There are similarities between the growth processes of ingot crystalline silicon with different sizes, so the growth law of small size crystals could be transferred to large size crystals. In this paper, transfer learning (TL) was used to design the hot zone of the G8 ingot furnace. The design targeted parameters are the positions and volumes of the side and top heaters, and the height of the partition block on the side insulation cage. The main design goals are to reduce the dislocation defects inside the crystal, suppress polycrystalline at the edge of the silicon ingot and make the solid-liquid interface slightly convex. First, a neural network was used to establish a mapping model between the hot zone geometric parameters and hot zone evaluation parameters for the existing G7 ingot furnace. Then the model was transferred to the G8 ingot furnace. The effects of different model structures on the transfer process were analyzed. Then Dropout was used to determine whether the model is overfitting. The genetic algorithm (GA) and the clustering algorithm (CA) were applied to optimize the hot zone geometric parameters. The above is the process of G8 hot zone design. Finally, the numerical simulation method was used to study the temperature distribution and solid-liquid interface shape of the optimized schemes. The final selected scheme could achieve high quality crystal growth. Then the scheme was applied to the hot zones of G7 and G8 furnaces. The results show that the axial temperature gradient of G8 in silicon melt and silicon crystal is smaller than that of G7, and the radial temperature gradient at the solid-liquid interface is also smaller than that of G7. It is beneficial to reduce the thermal stress inside the crystal.

Key words: G8 ingot furnace, crystalline silicon, hot zone design, transfer learning, neural network, genetic algorithm