89 research outputs found
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Interpreting the Operando XANES of Surface-Supported Subnanometer Clusters: When Fluxionality, Oxidation State, and Size Effect Fight
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
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Oxidative Dehydrogenation of Cyclohexane by Cu vs Pd Clusters: Selectivity Control by Specific Cluster Dynamics
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
Perspective: Size selected clusters for catalysis and electrochemistry
Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster–support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications
A high-speed tunable beam splitter for feed-forward photonic quantum information processing
We realize quantum gates for path qubits with a high-speed,
polarization-independent and tunable beam splitter. Two electro-optical
modulators act in a Mach-Zehnder interferometer as high-speed phase shifters
and rapidly tune its splitting ratio. We test its performance with heralded
single photons, observing a polarization-independent interference contrast
above 95%. The switching time is about 5.6 ns, and a maximal repetition rate is
2.5 MHz. We demonstrate tunable feed-forward operations of a single-qubit gate
of path-encoded qubits and a two-qubit gate via measurement-induced interaction
between two photons
Nanoassemblies of ultrasmall clusters with remarkable activity in carbon dioxide conversion into C1 fuels
Cu nanoassemblies formed transiently during reaction from size-selected subnanometer Cu4 clusters supported on amorphous OH-terminated alumina convert CO2 into methanol and hydrocarbons under near-atmospheric pressure at rates considerably higher than those of individually standing Cu4 clusters. An in situ characterization reveals that the clusters self-assemble into 2D nanoassemblies at higher temperatures which then disintegrate upon cooling down to room temperature. DFT calculations postulate a formation mechanism of these nanoassemblies by hydrogen-bond bridges between the clusters and H2O molecules, which keep the building blocks together while preventing their coalescence
Experimental delayed-choice entanglement swapping
Motivated by the question, which kind of physical interactions and processes
are needed for the production of quantum entanglement, Peres has put forward
the radical idea of delayed-choice entanglement swapping. There, entanglement
can be "produced a posteriori, after the entangled particles have been measured
and may no longer exist". In this work we report the first realization of
Peres' gedanken experiment. Using four photons, we can actively delay the
choice of measurement-implemented via a high-speed tunable bipartite state
analyzer and a quantum random number generator-on two of the photons into the
time-like future of the registration of the other two photons. This effectively
projects the two already registered photons onto one definite of two mutually
exclusive quantum states in which either the photons are entangled (quantum
correlations) or separable (classical correlations). This can also be viewed as
"quantum steering into the past"
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