DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology

Maggipinto, Marco; Beghi, Alessandro; McLoone, Seán; Susto, Gian Antonio

doi:10.1016/j.jprocont.2019.08.006

Cited by 41 publications

(18 citation statements)

References 30 publications

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“…Since machine data is typically sampled much more often when compared to metrology data, and since machine data becomes immediately available when compared to the delays that frequently occur with metrology tools, an accurate virtual metrology is capable of meaningfully developing the process control and monitoring performance through a constantly supply of real-time forecasted metrology data. A few feature extraction methods for virtual metrology with multisensor data are proposed in [17,115,116].…”

Section: Data Mining Applications For Metrology Measurement and Insmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

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Section: Data Mining Applications For Metrology Measurement and Insmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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“…In [41], the authors presented the use of deep learning (DL) in VM for feature extraction. According to the authors, although VM has been widely studied, wide-scale implementation of this enabling technology in the production environment has yet to be successful.…”

Section: Related Workmentioning

confidence: 99%

“…Various algorithms were also proposed in the literature to perform this feature filtering process. For example, reference [27] proposed a feature selection algorithm that incorporates randomization for search efficiency, reference [28] applied fused LASSO algorithm to address this issue, while references [39], [41], and [45] employed deep learning models to deal with this issue.…”

Section: (E) Process Representatives Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective

Tin

Tan

Yong³

et al. 2021

IEEE Access

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Integrated circuits (IC) are fabricated on a wafer through stacked layers of circuit patterns. To ensure proper functionality, the overlay of each pattern layer must be within the tolerance. Inspecting each wafer's overlay is unrealistic and impractical. Hence, wafers are selectively inspected at metrology stations through sampling strategies. With virtual metrology (VM), the metrology quality of the uninspected wafers can be estimated. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a VM to estimate the overlay of the photolithography process is envisioned. Past researches on overlay VM leveraged fault detection and classification (FDC) data to estimate the overlay errors. As such, for fabs in the progress of completing their FDC development for photolithography equipment, a different modeling approach is required to realize an overlay VM that sustains the production line until FDC data can be leveraged for VM. With practical gaps that must be addressed in real fabs, this paper focuses on realizing an overlay VM for the photolithography process without leveraging FDC data. Therefore, the objectives of this paper are two folds: First, to identify the research challenges towards realizing the overlay VM. Second, to propose the future research perspectives of the envisioned overlay VM. Based on the future research perspectives, a two-steps overlay VM modeling approach utilizing data mining techniques is proposed toward realizing the envisioned overlay VM system. The proposed approach first classifies the process stability at the wafer lot level, and subsequently, performs overlay error estimations for wafers in the wafer lots classified with stable process. Linear regression models are proposed to perform overlay error estimations in this work to augment the interpretability of the overlay VM.

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“…Currently, through the use of classical images it is possible to determine erosion and land use chang [ 38 , 39 ]. Additionally, for ANN metrology, its possible to determine imprecise temporal-spatial parameters on images [ 40 , 41 ]. For this reason, one improving spatial solution on adverse resolution conditions are the implementation of Recurrent Neural Networks (RNN), Deep Reinforcement Learning (DRL), and Convolutional Neural Network (CNN) [ 42 , 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks

Marques

Souza

Müller

et al. 2020

Sensors

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Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset.

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DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology

Cited by 41 publications

References 30 publications

A Review of Data Mining Applications in Semiconductor Manufacturing

A Review of Data Mining Applications in Semiconductor Manufacturing

A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective

Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks

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