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In this paper, a new phononic crystal structure with periodic units consisting of four tungsten vibrators wrapped by silicone rubber coating is designed. The dispersion curve, vibration mode and transmission loss spectrum of the structure were calculated by finite-element method(FEM). The results show that the bandgap range of this structure is 18. 85 ~225. 28 Hz, which is consistent with the frequency attenuation range of transmission loss spectrum, and can effectively suppress the propagation of elastic waves of 20 ~200 Hz in phononic crystals. By analyzing the vibration mode corresponding to the point on the dispersion curve, the formation mechanism of the bandgap is explained. The influence of the notch angle of the phononic crystal plate and the longitudinal and transverse spacing between the phonons on the bandgap was discussed in this paper. The results show that as the notch angle decreases, the lower boundary of the bandgap remains almost unchanged, and the upper boundary of the bandgap rises, thus increasing the width of the bandgap. As the transverse or longitudinal spacing between the vibrators increases, the lower and upper boundaries of the bandgap rise, and the width of the bandgap increases, thus the bandgap of the phononic crystal model is optimized. At the same time, the notch design of the phononic crystal plate save the material and reduce the weight of the structure. [ABSTRACT FROM AUTHOR]

In the process of preparing silicon single crystal, the Czochralski method has problems such as multiple mechanism assumptions, unclear boundary conditions under multiple field couplings, interlaced and mutual influence of physical and chemical changes, which makes it impossible to establish an accurate mechanism model for silicon single crystal growth process control. To solve this problem, based on a large amount of crystal growth data in the single crystal furnace pulling workshop, this paper analyzes the characteristic parameters related to crystal diameter based on the maximum information coefficient (MIC) algorithm proposed by the mutual information theory. Then, based on the nonlinear autoregressive with exogeneous inputs (NARX) dynamic neural network, a multi-input, and single-output equal-diameter phase crystal diameter prediction model was established, the diameter prediction was also performed for the three single crystal furnace pulling data, and the average mean square error value of the prediction is 0.000774. Finally, the prediction model of crystal diameter in the equal diameter stage established based on the NARX dynamic neural network is compared with the prediction model of crystal diameter in the equal diameter stage established based on the back propagation (BP) neural network. The results of the comparative analysis verified the superiority of the NARX dynamic neural network model for predicting the crystal diameter in the isodiametric stage. The results show the NARX dynamic neural network provides a more accurate identification model for the control of crystal diameter. [ABSTRACT FROM AUTHOR]

Ingot crystalline silicon is one of the important sources for solar-grade crystalline silicon materials. For reducing the cost of silicon wafers, it is necessary to develop large-size ingot crystalline silicon while ensuring the crystal quality. The core parameters that affect the quality of cast crystalline silicon include the ratio of crystal growth rate to temperature gradient at the solidification interface V/ G, wall heat flow q, height difference of solidification interface Δh, and temperature difference of silicon melt ΔT. Aiming at the quality control during the growth of ingot crystalline silicon, this study established a process control optimization method based on the artificial neural network (ANN) model for the crystal growth process. A process control data set for the growth process of ingot crystalline silicon was constructed based on the experimental measurement data and numerical simulation results. The opening of the bottom insulation cage and the power ratio of the side and top heaters were used as the main process control parameters, and V/ G, | q |, | Δh |, ΔT are the optimization goals. A neural network model for mapping the relationship between process control parameters and thermal field characteristics was established. The influence of the bottom heat insulation cage opening and the power ratio of the side to top heaters on the thermal field during the crystal growth process were analyzed using the trained neural network model. The process control parameters for the ingot crystalline silicon growth process are optimized using genetic algorithm (GA), improving the quality of grown crystal and reducing energy consumption. Finally based on the experimental test images, the influences of V/ G on the crystal quality were discussed. Research shows that V/ G in the middle stage of crystal growth changes smoothly along the horizontal direction, and the corresponding defects are less and evenly distributed. Therefore, increasing the uniformity of V/ G in the horizontal direction is also an important factor to improve the quality of crystal. [ABSTRACT FROM AUTHOR]