Processing Methods of Silicon to its Ingot: a Review

Prakash, Vemuri; Agarwal, Abhishek; Mussada, Eswara Krishna

doi:10.1007/s12633-018-9983-0

Cited by 15 publications

(1 citation statement)

References 146 publications

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“…1f . Most available bulk (boule) growth information is for silicon, due to its technological importance, with Czochralski 23 , Bridgeman 24 , 25 and float zone (FZ) 26 methods prevalent, and both the scientific and patent literature are valuable background resources. Liquid phase epitaxy (LPE) was developed for growth of layered structures in semiconductors, often at temperatures below the bulk melting point of the seed wafer.…”

Section: History and Backgroundmentioning

confidence: 99%

Semiconductor core fibres: materials science in a bottle

2021

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Novel core fibers have a wide range of applications in optics, as sources, detectors and nonlinear response media. Optoelectronic, and even electronic device applications are now possible, due to the introduction of methods for drawing fibres with a semiconductor core. This review examines progress in the development of glass-clad, crystalline core fibres, with an emphasis on semiconducting cores. The underlying materials science and the importance of post-processing techniques for recrystallization and purification are examined, with achievements and future prospects tied to the phase diagrams of the core materials.

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Section: History and Backgroundmentioning

confidence: 99%

Semiconductor core fibres: materials science in a bottle

2021

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Multi-label oxide classification in float-zone silicon crystal growth using transfer learning and asymmetric loss

Chen,

Tosello,

Calaon

2024

J Intell Manuf

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Float-Zone (FZ) crystal growth process allows for producing higher purity silicon crystal with much lower concentrations of impurities, in particular low oxygen content. Nevertheless, the FZ process occasionally faces the problem of small contamination from oxidation. This can come in the form of a thin oxide layer that may form on un-melted polysilicon surface. The appearance of the oxide layer indicates degraded machine performance and the need for machine maintenance. Therefore, oxide investigation is important for improving both the FZ process and FZ machines, and the first step is oxide recognition. In this study, we characterized oxide into mainly three varieties, according to their surface texture characteristics, which are: (i) spot (ii) shadow and (iii) ghost curtain. We leveraged FZ images captured from the vision system integrated on the FZ machine to establish an oxide dataset. Targeted for data imbalance problem in our dataset, a method based on transfer learning and asymmetric loss for multi-label oxide classification is presented in this work. The results showed that the pre-trained model and the asymmetric loss used for training outperformed the baseline models and improved the classification performance. Furthermore, this study deeply investigated the effectiveness of the components of asymmetric loss. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to explain decision process of the models in order to adopt them in the industry.

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