273 research outputs found
Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization
As semiconductor patterning dimensions shrink, more advanced Scanning
Electron Microscopy (SEM) image-based defect inspection techniques are needed.
Recently, many Machine Learning (ML)-based approaches have been proposed for
defect localization and have shown impressive results. These methods often rely
on feature extraction from a full SEM image and possibly a number of regions of
interest. In this study, we propose a deep Reinforcement Learning (RL)-based
approach to defect localization which iteratively extracts features from
increasingly smaller regions of the input image. We compare the results of 18
agents trained with different feature extractors. We discuss the advantages and
disadvantages of different feature extractors as well as the RL-based framework
in general for semiconductor defect localization.Comment: 5 pages, 5 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
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
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
A route towards the fabrication of 2D heterostructures using atomic layer etching combined with selective conversion
Heterostructures of low-dimensional semiconducting materials, such as transition metal dichalcogenides (MX2), are promising building blocks for future electronic and optoelectronic devices. The patterning of one MX2 material on top of another one is challenging due to their structural similarity. This prevents an intrinsic etch stop when conventional anisotropic dry etching processes are used. An alternative approach consist in a two-step process, where a sacrificial silicon layer is pre-patterned with a low damage plasma process, stopping on the underlying MoS2 film. The pre-patterned layer is used as sacrificial template for the formation of the top WS2 film. This study describes the optimization of a cyclic Ar/Cl2 atomic layer etch process applied to etch silicon on top of MoS2, with minimal damage, followed by a selective conversion of the patterned Si into WS2. The impact of the Si atomic layer etch towards the MoS2 is evaluated: in the ion energy range used for this study, MoS2 removal occurs in the over-etch step over 1–2 layers, leading to the appearance of MoOx but without significant lattice distortions to the remaining layers. The combination of Si atomic layer etch, on top of MoS2, and subsequent Si-to-WS2 selective conversion, allows to create a WS2/MoS2 heterostructure, with clear Raman signals and horizontal lattice alignment. These results demonstrate a scalable, transfer free method to achieve horizontally individually patterned heterostacks and open the route towards wafer-level processing of 2D materials
Atomic layer deposition applications 12
The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus and the risk for post-transplant diabetes. Artificial neural network (ANN) and logistic regression (LR) models were used to predict the bioavailability of tacrolimus and risk for post-transplant diabetes, respectively. The five-fold cross-validation of ANN model showed good correlation with the experimental data of bioavailability (r2 = 0.93-0.96). Younger age, male gender, optimal body mass index were shown to exhibit lower bioavailability of tacrolimus. ABCB1 1236 C>T and 2677G>T/A showed inverse association while CYP3A5*3 showed a positive association with the bioavailability of tacrolimus. Gender bias was observed in the association with ABCB1 3435 C>T polymorphism. CYP3A5*3 was shown to interact synergistically in increasing the bioavailability in combination with ABCB1 1236 TT or 2677GG genotypes. LR model showed an independent association of ABCB1 2677 G>T/A with post transplant diabetes (OR: 4.83, 95% CI: 1.22-19.03). Multifactor dimensionality reduction analysis (MDR) revealed that synergistic interactions between CYP3A5*3 and ABCB1 2677 G>T/A as the determinants of risk for post-transplant diabetes. To conclude, the ANN and MDR models explore both individual and synergistic effects of variables in modulating the bioavailability of tacrolimus and risk for post-transplant diabetes
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