Growth of Crystalline Silicon for Solar Cells: Czochralski Si

Yu, Xuegong; Yang, Deren

doi:10.1007/978-3-662-56472-1_12

Cited by 2 publications

(3 citation statements)

References 69 publications

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“…Similarly, thermal shock is also a result of many actions that can be involved during growth. Although Czochralski Si growth is well-explained in some articles and book chapters [3,[12][13][14][15], the generation of dislocations was poorly clarified. Still, few articles are devoted to covering the critical issues of Cz-Si growth such as structure loss.…”

Section: Introductionmentioning

confidence: 99%

Surface Examination of Structure Loss in N-Type Czochralski Silicon Ingots

Hendawi,

Stokkan,

Øvrelid

et al. 2024

SiliconPV Conf Proc

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In principle, growing a dislocation-free Czochralski silicon ingot is possible if the growth process is kept stable and below the critical resolved shear stress value. However, in practice, a considerable proportion of the Si ingots are remelted due to the generation of dislocations or the so-called structure loss. The assessment of the failed ingots is a crucial step toward higher yield. However, the characterization of Si ingots is challenging due to their high brittleness and the high concentration of dislocations related to slip. In this work, we develop a non-destructive method to investigate the ingots that have experienced structure loss and reveal the root causes of this failure. Many characteristic features have been found on the surface of Czochralski silicon ingots. Based on these features, the ingots are classified into seven major groups that could be related to the main causes of the structure loss. Furthermore, the temperature gradient of several ingots is revealed by careful measurements of the growth ridges’ widths of these ingots. The results show that most of the failed ingots experience low-temperature gradients before the dislocation generation which agrees with the previous results. Three ingots have a clear particle hit on the surface, which caused an immediate transition to a multi-crystalline silicon structure. Particles are found on atomically smooth and rough interfaces, growth ridges, and surfaces in between. The surface examination method is a promising, fast, low-cost, and non-destructive technique that can be used to identify the most critical factors of structure loss in industrial ingots.

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Section: Introductionmentioning

confidence: 99%

Surface Examination of Structure Loss in N-Type Czochralski Silicon Ingots

Hendawi,

Stokkan,

Øvrelid

et al. 2024

SiliconPV Conf Proc

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“…Consequently, it took 40 years to increase the silicon crystal diameter from 1 to 12 in. [7,8] In this study, the complex nonlinear relationship between crystal growth process parameters, furnace geometry, the s/l interface shape (Figure 2b), and the Voronkov criterion v/G [9,10] for defect-free silicon crystal was derived using artificial neural networks (ANN) and dimensionless numbers. For v/G values greater than 1.34 × 10 −3 cm 2 K −1 min −1 , the silicon crystal is vacancy-rich, while for v/G less than 1.34…”

Section: Introductionmentioning

confidence: 99%

“…The influence of the mentioned individual parameters on Cz–Si crystal quality has been the focus of extensive experimental and numerical research in the past, e.g.. [ 7,8,11–34 ] Typically, these studies have centered on assessing the effects of the parameter under investigation within the context of specific crystal and/or crucible sizes.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Cz–Si Scale‐Up under Conditions of Partial Similarity

Dropka,

Böttcher,

Chappa

et al. 2024

Cryst. Res. Technol.

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In Cz–Si growth, the shape of the solid–liquid interface and the v/G ratio significantly impact crystal quality. This study utilizes a data‐driven approach, employing multilayer perceptron (MLP) neural networks and Bayesian optimization, to investigate the scale‐up process of Cz–Si under conditions of partial similarity. The focus is on exploring the influence of various process and furnace geometry parameters, as well as radiation shield material properties, on the critical measures of crystal quality. Axisymmetric CFD modeling produces 340 sets of 18D raw data, from which 14‐dimensionless derived data tuples are generated for the design and training of the MLP. The best MLP obtained demonstrates the ability to accurately assess the complex nonlinear dependencies among dimensionless numbers derived from CFD data and, on the output side, interface deflection and v/G. These relationships, crucial for scale‐up, are successfully generalized across a wide range of parameters.

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Growth of Crystalline Silicon for Solar Cells: Czochralski Si

Cited by 2 publications

References 69 publications

Surface Examination of Structure Loss in N-Type Czochralski Silicon Ingots

Surface Examination of Structure Loss in N-Type Czochralski Silicon Ingots

Data‐Driven Cz–Si Scale‐Up under Conditions of Partial Similarity

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