A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers

IF 1.9 Q3 PHYSICS, APPLIED
Alexandra Wörnhör, M. Demant, H. Vahlman, S. Rein
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引用次数: 1

Abstract

Epitaxially-grown wafers on top of sintered porous silicon are a material-efficient wafer production process, that is now being launched into mass production. This production process makes the material-expensive sawing procedure obsolete since the wafer can be easily detached from its seed substrate. With high-throughput inline production processes, fast and reliable evaluation processes are crucial. The quality of the porous layers plays an important role regarding a successful detachment. Therefore, we present a fast and non-destructive investigation algorithm of thin, porous silicon layers. We predict the layer parameters directly from inline reflectance data by using a convolutional neural network (CNN), which is inspired by a comprehensive optical modelling approach from literature. There, a numerical fitting approach on reflection curves calculated with a physical model is performed. By adding the physical model to the CNN, we create a hybrid model, that not only predicts layer parameters, but also recalculates reflection curves. This allows a consistency check for a self-supervised network optimization. Evaluation on experimental data shows a high similarity with Scanning Electron Microscopy (SEM) measurements. Since parallel computation is possible with the CNN, 30.000 samples can be evaluated in roughly 100 ms.
一个自一致的混合模型连接经验和光学模型快速,非破坏性的在线表征薄,多孔硅层
在烧结多孔硅上外延生长晶圆是一种材料高效的晶圆生产工艺,目前正投入大规模生产。这种生产过程使得材料昂贵的锯切过程过时,因为晶圆片可以很容易地从它的种子基板上分离出来。在高通量的在线生产过程中,快速可靠的评估过程至关重要。多孔层的质量对成功剥离起着重要的作用。因此,我们提出了一种快速、无损的薄多孔硅层检测算法。我们使用卷积神经网络(CNN)直接从内联反射率数据预测层参数,这是受到文献中综合光学建模方法的启发。在此基础上,对物理模型计算的反射曲线进行了数值拟合。通过将物理模型添加到CNN中,我们创建了一个混合模型,该模型不仅可以预测层参数,还可以重新计算反射曲线。这允许对自监督网络优化进行一致性检查。实验数据与扫描电子显微镜(SEM)测量结果高度相似。由于CNN可以进行并行计算,因此可以在大约100毫秒内评估30,000个样本。
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来源期刊
EPJ Photovoltaics
EPJ Photovoltaics PHYSICS, APPLIED-
CiteScore
2.30
自引率
4.00%
发文量
15
审稿时长
8 weeks
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