An official website of the United States government
Abstract This paper introduces a novel wafer-edge quality inspection method based on analysis of curved-edge diffractive fringe patterns, which occur when light is incident and diffracts around the wafer edge. The proposed method aims to identify various defect modes at the wafer edges, including particles, chipping, scratches, thin-film deposition, and hybrid defect cases. The diffraction patterns formed behind the wafer edge are influenced by various factors, including the edge geometry, topography, and the presence of defects. In this study, edge diffractive fringe patterns were obtained from two approaches: (1) a single photodiode collected curved-edge interferometric fringe patterns by scanning the wafer edge and (2) an imaging device coupled with an objective lens captured the fringe image. The first approach allowed the wafer apex characterization, while the second approach enabled simultaneous localization and characterization of wafer quality along two bevels and apex directions. The collected fringe patterns were analyzed by both statistical feature extraction and wavelet transform; corresponding features were also evaluated through logarithm approximation. In sum, both proposed wafer-edge inspection methods can effectively characterize various wafer-edge defect modes. Their potential lies in their applicability to online wafer metrology and inspection applications, thereby contributing to the advancement of wafer manufacturing processes.
more »
« less
Wafer-Scale Fabrication of Quantum Photonic Devices in Silicon Carbide
We develop a wafer-scale process for nanofabrication of color center photonic devices in an arbitrary silicon carbide substrate using a reactive ion beam etching approach with a rotating tilted wafer.
more »
« less
- Award ID(s):
- 2047564
- PAR ID:
- 10481325
- Publisher / Repository:
- Optica Publishing Group
- Date Published:
- ISBN:
- 978-1-957171-29-6
- Page Range / eLocation ID:
- JTu5A.40
- Format(s):
- Medium: X
- Location:
- Tacoma, Washington
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Wafer quality control is one of the important processes to improve the yield rate of semiconductor products. Profile quality and defects in the wafer are two key factors that should be taken into consideration. In this research, we introduce a method that measures the profile of the upper surface and the thickness of the wafer at the same time using an optical fiber cascaded Fabry–Pérot interferometer working at wavelength of 1550 nm. Therefore, the 3D profile of the wafer can be reconstructed directly. Testing results show that both accuracy and precision of the Fabry–Pérot interferometer are within a nanometer scale. Defects, especially those embedded inside the wafer, will be detected by monitoring the leaky field with treating wafers as slab waveguides. With the leaky field detection, defects on the lower surface of the wafer were successfully detected by monitoring the leaky field above the upper surface of the wafer. Compared with traditional methods such as radiographic testing or computed tomography testing, the proposed methods provide a cost-effective alternative for wafer quality evaluation.more » « less
-
In smart manufacturing, semiconductors play an indispensable role in collecting, processing, and analyzing data, ultimately enabling more agile and productive operations. Given the foundational importance of wafers, the purity of a wafer is essential to maintain the integrity of the overall semiconductor fabrication. This study proposes a novel automated visual inspection (AVI) framework for scrutinizing semiconductor wafers from scratch, capable of identifying defective wafers and pinpointing the location of defects through autonomous data annotation. Initially, this proposed methodology leveraged a texture analysis method known as gray-level co-occurrence matrix (GLCM) that categorized wafer images—captured via a stroboscopic imaging system—into distinct scenarios for high- and low-resolution wafer images. GLCM approaches further allowed for a complete separation of low-resolution wafer images into defective and normal wafer images, as well as the extraction of defect images from defective low-resolution wafer images, which were used for training a convolutional neural network (CNN) model. Consequently, the CNN model excelled in localizing defects on defective low-resolution wafer images, achieving an F1 score—the harmonic mean of precision and recall metrics—exceeding 90.1%. In high-resolution wafer images, a background subtraction technique represented defects as clusters of white points. The quantity of these white points determined the defectiveness and pinpointed locations of defects on high-resolution wafer images. Lastly, the CNN implementation further enhanced performance, robustness, and consistency irrespective of variations in the ratio of white point clusters. This technique demonstrated accuracy in localizing defects on high-resolution wafer images, yielding an F1 score greater than 99.3%.more » « less
-
Predicting the minimum operating voltage Vmin of chips stands as a crucial technique in enhancing the speed and reliability of manufacturing testing flow. However, existing Vmin prediction methods often overlook various sources of variations in both training and deployment phases. Notably, overlooking wafer zone-to-zone (intra-wafer) variations and wafer-to-wafer (inter-wafer) variations diminishes the accuracy, data efficiency, and reliability of Vmin predictors. To address this challenge, we propose Restricted Bias Alignment (RBA), a novel data-efficient Vmin prediction framework that introduces a variation alignment technique to simultaneously estimate inter- and intra-wafer variations. Furthermore, we propose utilizing class probe data to model inter-wafer variations for the first time.more » « less
-
Wafer map pattern recognition is instrumental for detecting systemic manufacturing process issues. However, high cost in labeling wafer patterns renders it impossible to leverage large amounts of valuable unlabeled data in conventional machine learning based wafer map pattern prediction. We proposed a contrastive learning framework for semi-supervised learning and prediction of wafer map patterns. Our framework incorporates an encoder to learn good representation for wafer maps in an unsupervised manner, and a supervised head to recognize wafer map patterns. In particular, contrastive learning is applied for the unsupervised encoder representation learning supported by augmented data generated by different transformations (views) of wafer maps. We identified a set of transformations to effectively generate similar variants of each original pattern. We further proposed a novel rotation-twist transformation to augment wafer map data by rotating each given wafer map for which the angle of rotation is a smooth function of the radius. Experimental results demonstrate that the proposed semi-supervised learning framework greatly improves recognition accuracy compared to traditional supervised methods, and the rotation-twist transformation further enhances the recognition accuracy in both semi-supervised and supervised tasks.more » « less
