Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE

The technological advance of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) has opened the gates to extensive researches on thinner photoresists (below 30nm), necessary for the industrial implementation of High NA EUVL. Consequently, images from Scanning Electron Microscopy (SEM) suffer from reduced imaging contrast and low Signal-to-Noise Ratio (SNR), impacting the measurement of unbiased Line Edge Roughness (uLER) and Line Width Roughness (uLWR). Thus, the aim of this work is to enhance the SNR of SEM images by using a Deep Learning denoiser and enable robust roughness extraction of the thin resist. For this study, we acquired SEM images of Line-Space (L/S) patterns with a Chemically Amplified Resist (CAR) with different thicknesses (15nm, 20nm, 25nm, 30nm), underlayers (Spin-On-Glass-SOG, Organic Underlayer-OUL) and frames of averaging (4, 8, 16, 32, and 64 Fr). After denoising, a systematic analysis has been carried out on both noisy and denoised images using an open-source metrology software, SMILE 2.3.2, for investigating mean CD, SNR improvement factor, biased and unbiased LWR/LER Power Spectral Density (PSD). Denoised images with lower number of frames present unaltered Critical Dimensions (CDs), enhanced SNR (especially for low number of integration frames), and accurate measurements of uLER and uLWR, with the same accuracy as for noisy images with a consistent higher number of frames. Therefore, images with a small number of integration frames and with SNR < 2 can be successfully denoised, and advantageously used in improving metrology throughput while maintaining reliable roughness measurements for the thin resist

SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation

With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a new semiconductor defect inspection framework "SEMI-DiffusionInst" is investigated and compared to previous frameworks. To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Different feature extractor networks as backbones and data sampling strategies are investigated towards achieving a balanced trade-off between precision and computing efficiency. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes (per class APs). The bounding box and segmentation mAPs achieved by the proposed SEMI-DiffusionInst model are improved by 3.83% and 2.10%, respectively. Among individual defect types, precision on line collapse and thin bridge defects are improved approximately 15\% on detection task for both defect types. It has also been shown that by tuning inference hyperparameters, inference time can be improved significantly without compromising model precision. Finally, certain limitations and future work strategy to overcome them are discussed.Comment: 6 pages, 5 figures, To be published by IEEE in the proceedings of the 2023 ELMAR conferenc

Divergent proinflammatory immune responses associated with the differential susceptibility of cattle breeds to tuberculosis

Tuberculosis (TB) in the bovine is one of the most predominant chronic debilitating infectious diseases primarily caused by Mycobacterium bovis. Besides, the incidence of TB in humans due to M. bovis, and that in bovines (bovine TB, bTB) due to M. tuberculosis- indicates cattle as a major reservoir of zoonotic TB. While India accounts for the highest global burden of both TB and multidrug-resistant TB in humans, systematic evaluation of bTB prevalence in India is largely lacking. Recent reports emphasized markedly greater bTB prevalence in exotic and crossbred cattle compared to indigenous cattle breeds that represent more than one-third of the total cattle population in India, which is the largest globally. This study aimed at elucidating the immune responses underlying the differential bTB incidence in prominent indigenous (Sahiwal), and crossbred (Sahiwal x Holstein Friesian) cattle reared in India. Employing the standard Single Intradermal Tuberculin Test (SITT), and mycobacterial gene-targeting single as well as multiplex-PCR-based screening revealed higher incidences of bovine tuberculin reactors as well as Mycobacterium tuberculosis Complex specific PCR positivity amongst the crossbred cattle. Further, ex vivo mycobacterial infection in cultures of bovine peripheral blood mononuclear cells (PBMC) from SITT, and myco-PCR negative healthy cattle exhibited significantly higher intracellular growth of M. bovis BCG, and M. tuberculosis H37Ra in the crossbred cattle PBMCs compared to native cattle. In addition, native cattle PBMCs induced higher pro-inflammatory cytokines and signaling pathways, such as interferon-gamma (IFN-γ), interleukin-17 (IL-17), tank binding kinase-1 (TBK-1), and nitric oxide (NO) upon exposure to live mycobacterial infection in comparison to PBMCs from crossbred cattle that exhibited higher expression of IL-1β transcripts. Together, these findings highlight that differences in the innate immune responses of these cattle breeds might be contributing to the differential susceptibility to bTB infection, and the resultant disparity in bTB incidence amongst indigenous, and crossbred cattle

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

A Booster Vaccine Expressing a Latency-Associated Antigen Augments BCG Induced Immunity and Confers Enhanced Protection against Tuberculosis

BACKGROUND: In spite of a consistent protection against tuberculosis (TB) in children, Mycobacterium bovis Bacille Calmette-Guerin (BCG) fails to provide adequate protection against the disease in adults as well as against reactivation of latent infections or exogenous reinfections. It has been speculated that failure to generate adequate memory T cell response, elicitation of inadequate immune response against latency-associated antigens and inability to impart long-term immunity against M. tuberculosis infections are some of the key factors responsible for the limited efficiency of BCG in controlling TB. METHODS/PRINCIPAL FINDINGS: In this study, we evaluated the ability of a DNA vaccine expressing α-crystallin--a key latency antigen of M. tuberculosis to boost the BCG induced immunity. 'BCG prime-DNA boost' regimen (B/D) confers robust protection in guinea pigs along with a reduced pathology in comparison to BCG vaccination (1.37 log(10) and 1.96 log(10) fewer bacilli in lungs and spleen, respectively; p<0.01). In addition, B/D regimen also confers enhanced protection in mice. Further, we show that B/D immunization in mice results in a heightened frequency of PPD and antigen specific multi-functional CD4 T cells (3(+)) simultaneously producing interferon (IFN)γ, tumor necrosis factor (TNF)α and interleukin (IL)2. CONCLUSIONS/SIGNIFICANCE: These results clearly indicate the superiority of α-crystallin based B/D regimen over BCG. Our study, also demonstrates that protection against TB is predictable by an increased frequency of 3(+) Th1 cells with superior effector functions. We anticipate that this study would significantly contribute towards the development of superior booster vaccines for BCG vaccinated individuals. In addition, this regimen can also be expected to reduce the risk of developing active TB due to reactivation of latent infection

Latency Antigen α-Crystallin Based Vaccination Imparts a Robust Protection against TB by Modulating the Dynamics of Pulmonary Cytokines

BACKGROUND: Efficient control of tuberculosis (TB) requires development of strategies that can enhance efficacy of the existing vaccine Mycobacterium bovis Bacille Calmette Guerin (BCG). To date only a few studies have explored the potential of latency-associated antigens to augment the immunogenicity of BCG. METHODS/PRINCIPAL FINDINGS: We evaluated the protective efficacy of a heterologous prime boost approach based on recombinant BCG and DNA vaccines targeting α-crystallin, a prominent latency antigen. We show that "rBCG prime-DNA boost" strategy (R/D) confers a markedly superior protection along with reduced pathology in comparison to BCG vaccination in guinea pigs (565 fold and 45 fold reduced CFU in lungs and spleen, respectively, in comparison to BCG vaccination). In addition, R/D regimen also confers enhanced protection in mice. Our results in guinea pig model show a distinct association of enhanced protection with an increased level of interleukin (IL)12 and a simultaneous increase in immuno-regulatory cytokines such as transforming growth factor (TGF)β and IL10 in lungs. The T cell effector functions, which could not be measured in guinea pigs due to technical limitations, were characterized in mice by multi-parameter flow cytometry. We show that R/D regimen elicits a heightened multi-functional CD4 Th1 cell response leading to enhanced protection. CONCLUSIONS/SIGNIFICANCE: These results clearly indicate the superiority of α-crystallin based R/D regimen over BCG. Our observations from guinea pig studies indicate a crucial role of IL12, IL10 and TGFβ in vaccine-induced protection. Further, characterization of T cell responses in mice demonstrates that protection against TB is predictable by the frequency of CD4 T cells simultaneously producing interferon (IFN)γ, tumor necrosis factor (TNF)α and IL2. We anticipate that this study will not only contribute toward the development of a superior alternative to BCG, but will also stimulate designing of TB vaccines based on latency antigens

Deep Learning based SEM image Denoising Approaches for Improving Metrology and Inspection of Thin Resists

As semiconductor manufacturing is moving towards 3 nm nodes and beyond, the development of advanced metrology techniques to control the quality of fabricated features on silicon wafers is becoming more and more crucial. Among the popular metrology methods, Scanning Electron Microscopy is one of the most important techniques since it can produce images with resolutions as low as a few nanometers. Thus, SEM is very suitable to inspect the critical dimension of nanoscale printed structures. However, CD-SEM images intrinsically contain a significant amount of background noise due to various sources. This background noise can lead to inaccurate metrology and erroneous defect detection. There is a significant requisite to reduce the noise signal in CD-SEM images while keeping the actual morphology of the pattern feature unaltered. In this paper, we proposed two deep learning-based methods to denoise SEM images, one based on (1) supervised/semi-supervised learning technique, and the other based on (2) unsupervised learning. The two proposed methods were experimented with different noisy SEM images of categorically different geometrical patterns and have demonstrated exceptional performance in reducing noise both qualitatively and quantitatively. We also have demonstrated how our proposed deep learning denoiser is applicable towards challenging application scenarios such as defect inspection and contour extraction, specifically with thin resists, with significantly improved accuracy. First, we have validated that our proposed denoising techniques, especially the unsupervised model, are effective. The unsupervised training scheme also requires single noisy acquisitions to train denoising CNNs without any ground-truth or synthetic images, against previous supervised/semi-supervised data greedy approaches. Second, the proposed deep learning denoiser assisted framework allows improved defect inspection and contour extraction with thin resists. The goal of this work is to establish a robust de-noising technique to reduce the dependency of SEM image acquisition settings and to extract repeatable and accurate CDSEM metrology information for high NA EUV. Finally, the limitations of our proposed approaches and how to overcome them were also thoroughly discussed. </p