5 research outputs found
Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach
In this research work, we have demonstrated the application of Mask-RCNN
(Regional Convolutional Neural Network), a deep-learning algorithm for computer
vision and specifically object detection, to semiconductor defect inspection
domain. Stochastic defect detection and classification during semiconductor
manufacturing has grown to be a challenging task as we continuously shrink
circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect
inspection and analysis by state-of-the-art optical and e-beam inspection tools
is generally driven by some rule-based techniques, which in turn often causes
to misclassification and thereby necessitating human expert intervention. In
this work, we have revisited and extended our previous deep learning-based
defect classification and detection method towards improved defect instance
segmentation in SEM images with precise extent of defect as well as generating
a mask for each defect category/instance. This also enables to extract and
calibrate each segmented mask and quantify the pixels that make up each mask,
which in turn enables us to count each categorical defect instances as well as
to calculate the surface area in terms of pixels. We are aiming at detecting
and segmenting different types of inter-class stochastic defect patterns such
as bridge, break, and line collapse as well as to differentiate accurately
between intra-class multi-categorical defect bridge scenarios (as
thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as
well as thin resists (High NA applications). Our proposed approach demonstrates
its effectiveness both quantitatively and qualitatively.Comment: arXiv admin note: text overlap with arXiv:2206.1350
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review
A growing need exists for efficient and accurate methods for detecting
defects in semiconductor materials and devices. These defects can have a
detrimental impact on the efficiency of the manufacturing process, because they
cause critical failures and wafer-yield limitations. As nodes and patterns get
smaller, even high-resolution imaging techniques such as Scanning Electron
Microscopy (SEM) produce noisy images due to operating close to sensitivity
levels and due to varying physical properties of different underlayers or
resist materials. This inherent noise is one of the main challenges for defect
inspection. One promising approach is the use of machine learning algorithms,
which can be trained to accurately classify and locate defects in semiconductor
samples. Recently, convolutional neural networks have proved to be particularly
useful in this regard. This systematic review provides a comprehensive overview
of the state of automated semiconductor defect inspection on SEM images,
including the most recent innovations and developments. 38 publications were
selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of
these, the application, methodology, dataset, results, limitations and future
work were summarized. A comprehensive overview and analysis of their methods is
provided. Finally, promising avenues for future work in the field of SEM-based
defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection
Continual shrinking of pattern dimensions in the semiconductor domain is
making it increasingly difficult to inspect defects due to factors such as the
presence of stochastic noise and the dynamic behavior of defect patterns and
types. Conventional rule-based methods and non-parametric supervised machine
learning algorithms like KNN mostly fail at the requirements of semiconductor
defect inspection at these advanced nodes. Deep Learning (DL)-based methods
have gained popularity in the semiconductor defect inspection domain because
they have been proven robust towards these challenging scenarios. In this
research work, we have presented an automated DL-based approach for efficient
localization and classification of defects in SEM images. We have proposed
SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of
semiconductor wafer defects. The use of the proposed CN approach allows
improved computational efficiency compared to previously studied DL models.
SEMI-CN gets trained to output the center, class, size, and offset of a defect
instance. This is different from the approach of most object detection models
that use anchors for bounding box prediction. Previous methods predict
redundant bounding boxes, most of which are discarded in postprocessing. CN
mitigates this by only predicting boxes for likely defect center points. We
train SEMI-CN on two datasets and benchmark two ResNet backbones for the
framework. Initially, ResNet models pretrained on the COCO dataset undergo
training using two datasets separately. Primarily, SEMI-CN shows significant
improvement in inference time against previous research works. Finally,
transfer learning (using weights of custom SEM dataset) is applied from ADI
dataset to AEI dataset and vice-versa, which reduces the required training time
for both backbones to reach the best mAP against conventional training method
YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach
Shrinking pattern dimensions leads to an increased variety of defect types in
semiconductor devices. This has spurred innovation in patterning approaches
such as Directed self-assembly (DSA) for which no traditional, automatic defect
inspection software exists. Machine Learning-based SEM image analysis has
become an increasingly popular research topic for defect inspection with
supervised ML models often showing the best performance. However, little
research has been done on obtaining a dataset with high-quality labels for
these supervised models. In this work, we propose a method for obtaining
coherent and complete labels for a dataset of hexagonal contact hole DSA
patterns while requiring minimal quality control effort from a DSA expert. We
show that YOLOv8, a state-of-the-art neural network, achieves defect detection
precisions of more than 0.9 mAP on our final dataset which best reflects DSA
expert defect labeling expectations. We discuss the strengths and limitations
of our proposed labeling approach and suggest directions for future work in
data-centric ML-based defect inspection.Comment: 8 pages, 10 figures, accepted for the 38th EMLC Conference 202
EELWORM: a bioinspired multimodal amphibious soft robot
Exploration robots are challenged by a continuous adaptation to the terrain induced by ever changing environments. These adaptations can be subtle (e.g. when moving from a smooth to a rough terrain), however drastic changes in environment require robots to address different locomotion modes (e.g. crawling vs swimming). While each locomotion mode can be driven by a dedicated set of actuators, nature shows that multimodal locomotion is also possible by activating the same set of actuators in different sequences (e.g. swimming snakes). In this paper, we present EELWORM, a 40 cm long soft-bodied robot consisting out of an arrangement of five inflatable bending and elongating actuator modules that can be addressed individually. EELWORM is capable of both crawling and swimming by varying the actuation sequences within the same embodiment. We show multimodal locomotion at speeds of 2 body lengths per minute (crawling) and 3 body lengths per minute (swimming).status: publishe