Effective Additive-Free Acidic-Solution Texturing for Surface-Damage-Free Kerfless Silicon Wafers
Source abstract
Abstract
Crystalline solar cells have attracted significant attention in the solar-cell market owing to their low material cost. Multicrystalline silicon (mc-Si) is usually fabricated using the casting method, and approximately 35% of Si is removed as kerf in the sawing process performed to fabricate the wafer. The fabrication of kerfless mc-Si wafers does not involve the sawing and growing process. Hence, there is a significant reduction in the process cost (~50%), while saving ~66% energy in wafer manufacturing. Moreover, the absence of sawing process will result in no surface damage and smoother surfaces. Texturing using conventional methods such as acidic chemical etching are ineffective and as a result, the desired surface texture cannot be obtained. To solve this problem, the texture can be created using relatively expensive and complicated processes such as reactive-ion etching or metal-catalyzed chemical etching, which are not affected by the shape of the wafer surface. This article proposes a method for creating an effective texture that can increase the light absorptivity of mc-Si wafers manufactured by the kerfless method with no surface damage. This technology is a cost-effective and reliable process that uses an acidic chemical solution with no additive or mask. The etching rate and texture shape are investigated for different solution ratios of HF and HNO 3 . The surface morphology is examined using scanning electron microscopy, and the reflectance at 300-1200 nm is measured using ultraviolet-visible spectroscopy. A weighted average reflectance of ~20.0% is obtained, which reduces to ~7.4% on depositing an antireflective film.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Thin-Film Transistor Technologies · Nanowire Synthesis and Applications · Silicon Nanostructures and Photoluminescence
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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