Mr. Venkata Rama Samir Bhamidipati Profile

Venkata Rama Samir Bhamidipati

Staff Engineer at Siemens EDA

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Publications (12)

Proceedings Article | 20 November 2024 Poster + Paper

Automatic classification of EUV mask defects inspected using DUV inspection optics

Aravindh Rajiv, Samir Bhamidipati, Mark Pereira, Sankaranarayanan Paninjath, Neha Razdan, Stephen Kim, Jinhyuk Choi, Jonghoon Lim, Sujeong Won, Dongwook Lee, Woojin Kim, Jongha Park

Proceedings Volume 13216, 132162I (2024) https://doi.org/10.1117/12.3034707

KEYWORDS: Inspection, Extreme ultraviolet, Deep ultraviolet, Polarization, Image classification, Image processing

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Proceedings Article | 18 September 2024 Paper

Automatic wafer defect detection and accurate classification using machine learning based analysis of SEM images

Sanghyun Choi, Nathan Greeneltch, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, Ilhami Torunoglu

Proceedings Volume 13273, 132731L (2024) https://doi.org/10.1117/12.3031212

KEYWORDS: Data modeling, Scanning electron microscopy, Defect detection, Machine learning, Semiconducting wafers, Image classification

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Proceedings Article | 10 April 2024 Poster + Paper

Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Sanghyun Choi, Qian Xie, Nathan Greeneltch, Hyung Joo Lee, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, Ilhami Torunoglu

Proceedings Volume 12955, 129553M (2024) https://doi.org/10.1117/12.3012184

KEYWORDS: Education and training, Scanning electron microscopy, Data modeling, Performance modeling, Defect detection, Machine learning, Object detection, Semiconducting wafers, Image analysis, Image quality

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Proceedings Article | 5 October 2023 Paper

A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

Bappaditya Dey, Stewart Wu, Victor Blanco, Samir Bhamidipati, Amr Essam, Kevin Ahi, Sandip Halder, Germain Fenger

Proceedings Volume 12802, 1280208 (2023) https://doi.org/10.1117/12.2680884

KEYWORDS: Contour extraction, Scanning electron microscopy, Image quality, Deep learning, Denoising, Image processing, Data modeling, Image denoising, Defect detection

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Proceedings Article | 1 December 2022 Presentation + Paper

Benefits of SEM field-of-view contour averaging for contour-based MPC modeling

Kushlendra Mishra, Rachit Sharma, Ingo Bork, Zhiheng (Mary) Zuo, Mark Pereira, Samir Bhamidipati, Seshadri Rampoori

Proceedings Volume 12293, 122930H (2022) https://doi.org/10.1117/12.2641833

KEYWORDS: Calibration, Scanning electron microscopy, Photomasks, Statistical modeling, Signal to noise ratio, Metrology, Image quality, Data modeling, Critical dimension metrology

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Proceedings Article | 12 October 2021 Presentation + Paper

Unsupervised machine learning based SEM image denoising for robust contour detection

Bappaditya Dey, Stewart Wu, Sayantan Das, Kasem Khalil, Sandip Halder, Philippe Leray, Samir Bhamidipati, Kiarash Ahi, Mark Pereira, Germain Fenger, Magdy Bayoumi

Proceedings Volume 11854, 1185411 (2021) https://doi.org/10.1117/12.2600945

KEYWORDS: Scanning electron microscopy, Denoising, Machine learning, Bridges, Metrology, Stochastic processes, Image processing, Neural networks, Detection and tracking algorithms, Optical proximity correction

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Contour detection of an object is a fundamental computer vision problem in image processing domain. The goal is to find a concrete boundary for pixel ownership between an OOI (object-of-interest) and its corresponding background. However, contour extraction from low SN SEM images is a very challenging problem as different sources of noise shadow the estimation of underlying structural geometries. As device scaling continues to 3nm node and below, the extraction of accurate CD contour geometries from SEM images especially ADI (after developed inspection) is of utmost importance for a qualitative lithographic process as well as to verify device characterization in aggressive pitches. In this paper, we have applied a U-Net architecture based unsupervised machine learning approach for de-noising CD-SEM images. Unlike other discriminative deep-learning based de-noising approaches, the proposed method does not require any ground-truth as clean/noiseless images or synthetic noiseless images for training. Simultaneously, we have also attempted to demonstrate how de-noising is helping to improve the contour detection accuracy. We have analyzed and validated our result by using a programmable tool (SEMSuiteTM) for contour extraction. We have de-noised SEM images with categorically different geometrical patterns such as L/S (line-space), T2T (tip-to-tip), pillars with different scan types etc. and extracted the contours in both noisy and de-noised images. The comparative analysis demonstrates that de-noised images have higher confidence contour metric than their noisy twins while keeping the same parameter settings for both data input. When the ML algorithm is applied, the contour extraction results would have higher confidence numbers comparing with the ones only applied the conventional Gaussian or Median blur de-noise method. The final goal of this work is to establish a robust de-noising method to reduce the dependency of SEM image acquisition settings and provide more accurate metrology data for OPC calibration.

Proceedings Article | 22 February 2021 Presentation + Paper

Contour-based process characterization, modeling, and control for semiconductor manufacturing

Kan Zhou, Xin Guo, Wenzhan Zhou, Qijian Wan, Chunshan Du, Wenming Wu, Ao Chen, Recoo Zhang, Germain Fenger, Seshadri Rampoori, Bhamidipati Samir

Proceedings Volume 11611, 1161113 (2021) https://doi.org/10.1117/12.2591300

KEYWORDS: Process modeling, Semiconductor manufacturing, Control systems, Statistical analysis, Scanning electron microscopy, Process control, Calibration

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Proceedings Article | 13 October 2020 Presentation + Paper

Automatic classification of patterned mask defects for requalification at wafer fabs in absence of layout data

Samir Bhamidipati, Prakash Deep, Mark Pereira, Sankaranarayanan Paninjath, Peter Buck, Keitaro Katabuchi, Takenori Kato, Frank Wu, Chin Kuei Chang, Chain Ping Chen

Proceedings Volume 11518, 115180K (2020) https://doi.org/10.1117/12.2573122

KEYWORDS: Photomasks, Semiconducting wafers, Inspection, Image classification, Wafer inspection, Contamination, Air contamination, Image processing, Printing, Yield improvement

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Patterned masks require requalification at wafer fabrication plants. Periodic inspections are done at the wafer fab to identify any new defects, such as haze and contamination, which develop or get added on the mask due to their usage and the way they are handled. These defects, if not monitored over time, may result in mask defects that print on the wafer. It is thus mandatory to identify and fix them in their early stages. Repeated inspections, coupled with higher sensitivity inspections employed at wafer fabs, result in a large number of defects reported, which includes many small and faint defects. However, some of these small and faint defects need detailed operator review time, due to their potential to grow and have a larger impact later. Efficiency of classification includes both speed and accuracy. For a manual review, accuracy is primarily affected by consistency, arising due to the bulk and monotonous nature of classification task affected by human fatigue. The requirement for higher accuracy however, necessitates increased operator review and analysis time. Increased operator review time translates to the amount of time a mask in not used for printing wafers, i.e. productivity loss. Calibre® DefectClassify™ tool enables automatic classification of defects by employing stable algorithms to ensure consistency and accuracy, while algorithm efficiency ensures adequate speed. The tool thus aids in improving the throughput and yield at wafer fabs. The tool reads defect images, analyzes image properties to extract potential defect regions, processes the regions to identify actual defects and classifies them. This paper mainly focuses on the challenges faced in characterization and classification of defects from images reported by the inspection machine. The primary difference between analyzing inspections at wafer fab and mask shop is the availability of layout data. Unavailability of layout data complicates the tasks of identifying different pattern regions on the mask, especially assist features. With advanced technology nodes, the number of assist features present is higher while the features themselves get smaller in size. These features, if not identified correctly, may be mistaken as defects. Other than that, single die defects are a category that gets affected due to lack of layout information. Without a reference to compare with, these defects require separate sets of rules to be applied to images for their identification and classification. In particular, identification of defects on pattern edges and corners from single die images is challenging without a reference image.

Proceedings Article | 23 October 2015 Paper

Improvement in accuracy of defect size measurement by automatic defect classification

Bhamidipati Samir, Mark Pereira, Sankaranarayanan Paninjath, Chan-Uk Jeon, Dong-Hoon Chung, Gi-Sung Yoon, Hong-Yul Jung

Proceedings Volume 9635, 963520 (2015) https://doi.org/10.1117/12.2202511

KEYWORDS: Inspection, Photomasks, Defect inspection, Liquids, Coating, Image processing, Neodymium, Extreme ultraviolet, Semiconducting wafers, Optical properties

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The blank mask defect review process involves detailed analysis of defects observed across a substrate’s multiple preparation stages, such as cleaning and resist-coating. The detailed knowledge of these defects plays an important role in the eventual yield obtained by using the blank. Defect knowledge predominantly comprises of details such as the number of defects observed, and their accurate sizes. Mask usability assessment at the start of the preparation process, is crudely based on number of defects. Similarly, defect size gives an idea of eventual wafer defect printability. Furthermore, monitoring defect characteristics, specifically size and shape, aids in obtaining process related information such as cleaning or coating process efficiencies.

Blank mask defect review process is largely manual in nature. However, the large number of defects, observed for latest technology nodes with reducing half-pitch sizes; and the associated amount of information, together make the process increasingly inefficient in terms of review time, accuracy and consistency. The usage of additional tools such as CDSEM may be required to further aid the review process resulting in increasing costs.

Calibre® MDPAutoClassify™ provides an automated software alternative, in the form of a powerful analysis tool for fast, accurate, consistent and automatic classification of blank defects. Elaborate post-processing algorithms are applied on defect images generated by inspection machines, to extract and report significant defect information such as defect size, affecting defect printability and mask usability. The algorithm’s capabilities are challenged by the variety and complexity of defects encountered, in terms of defect nature, size, shape and composition; and the optical phenomena occurring around the defect [1].

This paper mainly focuses on the results from the evaluation of Calibre® MDPAutoClassify™ product. The main objective of this evaluation is to assess the capability of accurately estimating the size of the defect from the inspection images automatically. The sensitivity to weak defect signals, filtering out noise to identify the defect signals and locating the defect in the images are key success factors. The performance of the tool is assessed on programmable defect masks and production masks from HVM production flow. Implementation of Calibre® MDPAutoClassify™ is projected to improve the accuracy of defect size as compared to what is reported by inspection machine, which is very critical for production, and the classification of defects will aid in arriving at appropriate dispositions like SEM review, repair and scrap.

Proceedings Article | 9 July 2015 Paper

Automatic classification and accurate size measurement of blank mask defects

Samir Bhamidipati, Sankaranarayanan Paninjath, Mark Pereira, Peter Buck

Proceedings Volume 9658, 96580X (2015) https://doi.org/10.1117/12.2198001

KEYWORDS: Inspection, Photomasks, Coating, Particles, Image processing, Defect detection, Optical lithography, Photoresist processing, Visualization, Photoresist materials

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A blank mask and its preparation stages, such as cleaning or resist coating, play an important role in the eventual yield obtained by using it. Blank mask defects’ impact analysis directly depends on the amount of available information such as the number of defects observed, their accurate locations and sizes. Mask usability qualification at the start of the preparation process, is crudely based on number of defects. Similarly, defect information such as size is sought to estimate eventual defect printability on the wafer. Tracking of defect characteristics, specifically size and shape, across multiple stages, can further be indicative of process related information such as cleaning or coating process efficiencies. At the first level, inspection machines address the requirement of defect characterization by detecting and reporting relevant defect information. The analysis of this information though is still largely a manual process. With advancing technology nodes and reducing half-pitch sizes, a large number of defects are observed; and the detailed knowledge associated, make manual defect review process an arduous task, in addition to adding sensitivity to human errors. Cases where defect information reported by inspection machine is not sufficient, mask shops rely on other tools. Use of CDSEM tools is one such option. However, these additional steps translate into increased costs. Calibre NxDAT based MDPAutoClassify tool provides an automated software alternative to the manual defect review process. Working on defect images generated by inspection machines, the tool extracts and reports additional information such as defect location, useful for defect avoidance^[4][5]; defect size, useful in estimating defect printability; and, defect nature e.g. particle, scratch, resist void, etc., useful for process monitoring. The tool makes use of smart and elaborate post-processing algorithms to achieve this. Their elaborateness is a consequence of the variety and complexity of defects encountered. The variety arises due to factors such as defect nature, size, shape and composition; and the optical phenomena occurring around the defect. This paper focuses on preliminary characterization results, in terms of classification and size estimation, obtained by Calibre MDPAutoClassify tool on a variety of mask blank defects. It primarily highlights the challenges faced in achieving the results with reference to the variety of defects observed on blank mask substrates and the underlying complexities which make accurate defect size measurement an important and challenging task.

Proceedings Article | 17 October 2014 Paper

Automatic classification of blank substrate defects

Tom Boettiger, Peter Buck, Sankaranarayanan Paninjath, Mark Pereira, Rob Ronald, Dan Rost, Bhamidipati Samir

Proceedings Volume 9235, 92351J (2014) https://doi.org/10.1117/12.2069678

KEYWORDS: Inspection, Photomasks, Defect inspection, Image classification, Particles, Defect detection, Image processing, Manufacturing, Semiconducting wafers, Liquids

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Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask Technology Center (MPMask). The Calibre ADC tool was qualified on production mask blanks against the manual classification. The classification accuracy of ADC is greater than 95% for critical defects with an overall accuracy of 90%. The sensitivity to weak defect signals and locating the defect in the images is a challenge we are resolving. The performance of the tool has been demonstrated on multiple mask types and is ready for deployment in full volume mask manufacturing production flow. Implementation of Calibre ADC is estimated to reduce the misclassification of critical defects by 60-80%.

Proceedings Article | 8 November 2012 Paper

Key issues in automatic classification of defects in post-inspection review process of photomasks

Mark Pereira, Manabendra Maji, Ravi Pai, Samir B. V. R., Seshadri R., Pradeepkumar Patil

Proceedings Volume 8522, 85221Y (2012) https://doi.org/10.1117/12.964403

KEYWORDS: Inspection, Photomasks, Critical dimension metrology, Image registration, SRAF, Defect detection, Defect inspection, Opacity, Semiconducting wafers, Charge-coupled devices

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The mask inspection and defect classification is a crucial part of mask preparation technology and consumes a significant amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly precise mask inspection system with high detection sensitivity becomes greater. However, due to the high sensitivity, in addition to the detection of smaller defects on finer geometries, the inspection machine could report large number of false defects. The total number of defects becomes significantly high and the manual classification of these defects, where the operator should review each of the defects and classify them, may take huge amount of time. Apart from false defects, many of the very small real defects may not print on the wafer and user needs to spend time on classifying them as well. Also, sometimes, manual classification done by different operators may not be consistent. So, need for an automatic, consistent and fast classification tool becomes more acute in more advanced nodes. Automatic Defect Classification tool (NxADC) which is in advanced stage of development as part of NxDAT1, can automatically classify defects accurately and consistently in very less amount of time, compared to a human operator. Amongst the prospective defects as detected by the Mask Inspection System, NxADC identifies several types of false defects such as false defects due to registration error, false defects due to problems with CCD, noise, etc. It is also able to automatically classify real defects such as, pin-dot, pin-hole, clear extension, multiple-edges opaque, missing chrome, chrome-over-MoSi, etc. We faced a large set of algorithmic challenges during the course of the development of our NxADC tool. These include selecting the appropriate image alignment algorithm to detect registration errors (especially when there are sub-pixel registration errors or misalignment in repetitive patterns such as line space), differentiating noise from very small real defects, registering grey level defect images with layout data base, automatically finding out maximum critical dimension (CD) variation for defective patterns (where patterns could have Manhattan as well as all angle edges), etc. This paper discusses about many such key issues and suggests strategies to address some of them based upon our experience while developing the NxADC and evaluating it on production mask defects.

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