Exposure tool specific post-OPC verification

A scanning electron microscope (SEM) is the metrology tool used to accurately characterize very fine structures on wafers, usually by extracting one critical dimension (CD) per SEM image. This approach for optical proximity correction (OPC) modeling requires many measurements resulting in a lengthy cycle time for data collection, review, and cleaning, and faces reliability issues when dealing with critical two-dimensional (2-D) structures. An alternative to CD-based metrology is to use SEM image contours for OPC modeling. To calibrate OPC models with contours, reliable contours matched to traditional CD-SEM measurements are required along with a method to choose structure and site selections (number, type, and image space coverage) specific to a contour-based OPC model calibration. The potential of SEM contour model-based calibration is illustrated by comparing two contour-based models to reference models, one empirical model and a second rigorous simulation-based model. The contour-based models are as good as or better than a CD-based model with a significant advantage in the prediction of complex 2-D configurations with a reduced metrology work load.

As the semiconductor industry moves to the 45nm node and beyond, the tolerable lithography process window significantly shrinks due to the combined use of high NA and low k1 factor. This is exacerbated by the fact that the usable depth of focus at 45nm node for critical layer is 200nm or less. Traditional Optical Proximity Correction (OPC) only computes the optimal pattern layout to optimize its lithography patterning at nominal process condition (nominal defocus and nominal exposure dose) according to an OPC model calibrated at this nominal condition, and this may put the post-OPC layout at non-negligible patterning failure risk due to the inevitable process variation (mainly defocus and dose variations). With a little sacrifice at the nominal condition, process variation aware OPC can greatly enhance the robustness of post-OPC layout patterning in the presence of defocus and dose variation. There is also an increasing demand for through process window lithography verification for post-OPC circuit layout. The corner stone for successful process variation aware OPC and lithography verification is an accurately calibrated continuous process window model which is a continuous function of defocus and dose. This calibrated model needs to be able to interpolate and extrapolate in the usable process window. Based on Synopsys' OPC modeling software package-ProGen and ProGenPlus, we developed an automated process window (PW) modeling module, which can build process variation aware process window OPC model with continuously adjustable process parameters: defocus and dose. The calibration of this continuous PW model was performed in a single calibration process using silicon measurement at nominal condition and off-focus-off-dose conditions. Through the example of several process window models for layers at 45nm technology nodes, we demonstrated that this novel continuous PW modeling approach can achieve very good performance both at nominal condition and at interpolated or extrapolated off-focus-off-dose conditions.

The thermoluminescence dosimeter (TLD) has desirable features including low cost, reusability, small size, and relatively low energy dependence. However, the commonly available poly-crystal TLDs (e.g., TLD-100) exhibit high interdetector variability that requires individual calibration for high detection accuracy. To improve individual TLD tracking robustness, we developed an optical fingerprinting method to identify the TLD-100 chips. Seven hundred and fifty-two images were initially captured using a digital microscope camera to build a feature library for both facets of 376 TLD-100 chips. A median intensity thresholding method was used to segment images into foreground and background. The affine transformation was used to register the segmented images to the same position. The fingerprint of each image was calculated from its registered image. All fingerprints were then recorded in an Elasticsearch® search database. The TLD fingerprint match was tested three times when the library was established and repeated once 20months later. All chips were irradiated at 0, 1, 4, and 8Gy on a calibrated clinical MV linac to establish the individual calibration curve. The true positive rate of identifying TLDs based on their optical fingerprints was 100% at initialization of the inventory. After 20months and multiple deployments for characterization, calibration, and dose measurement, the true positive match rate dropped to 99% with zero false-positive matches. The TLDs exhibited high self-consistency in the dose-response test with R2 between 0.988 and 1 with linear regression. The TLD-100 chips surface textures are unique and sufficient to support accurate identification based on the optical fingerprinting. This method provides inexpensive and robust management of the TLDs for individual calibration and dosimetry.

In the integrated circuits manufacturing process, a critical dimension scanning electron microscope machine provides high-resolution images of geometries on semiconductor wafers and measurements of the printed polygons after the photolithography process. These images and measurements are crucial for calibrating lithography process models such as the etch model, optical proximity correction model, and resist model, which are necessary for simulating the photolithography process and hence reducing the probability of defects occurrence. However, The test chips used for model calibration often contain imperfections or anomalies that require accurate detection and classification to be removed from the model calibration process. Traditionally, engineers manually filter semiconductor wafer images and their measurements, which is a time-consuming and error-prone task. Therefore, there have been attempts to use machine learning to automate image filtering. In this research, we introduce a novel approach using an image preprocessing stage followed by a transformer-based object detection model to automate detecting and classifying defects in semiconductor wafer images containing an array of lines or a matrix of contacts. The proposed approach shows improved accuracy compared to traditional methods.

Driven by steadily decreasing critical dimensions in the semiconductor technology, sub-resolution assisting feature (SRAF) has been proposed to enlarge the capability of the exposure technique to smaller pitch requirements exceeding the standard lithography specification by improving the through-focus process window. There are generally two types of SRAF implementation methodologies: the rule-based approach and the model-based approach. In this paper, the hybrid SRAF implementation methodology is proposed to benefit from these two methodologies. Firstly, the candidate rules are selected by using the process window aware optical proximity correction (OPC) model. Secondly, the candidate SRAF rules are taped out. Finally, the optimized candidates among the taped out SRAF rules are verified by wafer data. If the wafer data show that the SRAF rule will not meet the criteria, the SRAF implementation methodology will go back to evaluate other candidates. From the wafer data, it is proved that the hybrid SRAF implementation methodology can achieve an optimized through-focus process window in a short turn around time (TAT) with lower engineering and mask cost.

An optical fingerprinting solution with a switchable backlight is demonstrated for liquid crystal displays (LCDs). In fingerprinting mode, the collimated infrared light is applied to improve the signal-noise ratio (SNR) between the valley and ridge of the fingerprint. Compared with the conventional backlight, the proposed structure can effectively improve the SNR. Furthermore, the fingerprinting mode and the display mode may work at the same time without interference because of the different wavelengths and light paths. It shows the great potential application of LCD-based optical fingerprints.

Purpose The purpose of this study was to ascertain the extent to which school-based speech-language pathologists' (SLPs) assessment practices with individual English learners (ELs) align with federal legislation and professional practice guidelines. Specifically, we were interested in examining SLPs' use of multiple tools during individual EL assessments, as well as relationships between practices and number of types of training experiences. Method School-based SLPs in a Midwestern state were recruited in person or via e-mail to complete an online survey pertaining to assessment. Of the 562 respondents who completed the survey, 222 (39.5%) indicated past or present experience with ELs, and thus, their data were included in the analyses. The questionnaire solicited information about respondent's demographics, caseload composition, perceived knowledge and skills and training experiences pertaining to working with ELs (e.g., graduate school, self-teaching, professional conferences), and assessment practices used in schools. Results The majority of respondents reported using multiple tools rather than a single tool with each EL they assess. Case history and observation were tools used often or always by the largest number of participants. SLPs who used multiple tools reported using both direct (e.g., standardized tests, dynamic assessment) and indirect tools (e.g., case history, interviews). Analyses revealed low to moderate positive associations between tools, as well as the use of speech-language samples and number of types of training experiences. Conclusions School-based SLPs in the current study reported using EL assessment practices that comply with federal legislation and professional practice guidelines for EL assessment. These results enhance our understanding of school-based SLPs' assessment practices with ELs and may be indicative of a positive shift toward evidence-based practice.

With several foundries/IDMs committed to using EUV to manufacture devices at the 7 nm and 5 nm nodes, the success of EUVL will depend critically on the ability of manufacturers to meet extremely tight edge placement error (EPE) budgets. EPE is affected by many factors and it becomes important to identify and address all systematic sources of edge placement error. One major source of this error, which hasn’t been given a lot of attention, continues to be the magnitude and variation of aberrations across the exposure field and between different scanners. EUV scanners are known to have significantly higher level of aberrations than DUV scanners due to the substantial drop in wavelength requiring tighter specifications on lens roughness, as well as a move to reflective optics producing double pass impact of surface roughness. While the EPE from variation in aberrations across the exposure field is correctable in OPC software, there are no known ways to address tool-to-tool aberration variation. Given that foundries are expected to have multiple EUV tools for high volume manufacturing, the degree of tool-matching between different machines is expected to play a critical role to the success of EUV. This work seeks to further the study by quantifying the simulated edge placement error on realistic 7 nm / 5 nm node designs resulting from a fleet consisting of multiple EUV tools, under the assumption of single OPC model / mask for multiple tools and whether such assumptions are valid. Given the importance of tool-to-tool aberration matching in EUVL, this study investigates the amount of variation in tool-to-tool aberration that can be tolerated before foundries must consider tool dedicated OPC mask sets. This study statistically analyzes different metrics such as EPEs, image shifts and worst case excursions to understand which single tool in the fleet should be best used in model calibration to generate the OPC mask shapes. In addition, an effort to rank relative quality of the verification solutions is investigated, to be used to tool allocation.

Automated Pipeline for the Deployment using OpenShift

Exploring teachers' perceptions on different CSCL script editing tools

Model-Driven Performance Engineering (MDPE) uses performance model interchange formats among multiple formalisms and tools to automate performance analysis. Model-to-Model (M2M) transformations convert system specifications into performance specifications and performance specifications to multiple performance model formalisms. Since a single tool is not good for everything, tools for different formalisms provide multiple solutions for evaluation and comparison. This chapter demonstrates transformations from the Performance Model Interchange Format (PMIF) into multiple formalisms: Queueing Network models solved with Java Modeling Tools (JMT), QNAP, and SPE·ED, and Petri Nets solved with PIPE2.

BioTechniquesVol. 59, No. 1 BioSpotlight / CitationsOpen AccessBioSpotlight / CitationsNathan S. Blow & Nijsje DormanNathan S. BlowSearch for more papers by this author & Nijsje DormanSearch for more papers by this authorPublished Online:3 Apr 2018https://doi.org/10.2144/000114304AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit Working with multiple data setsMuch recent effort has gone into identifying and characterizing DNA binding motifs, and a number of computer programs have been developed specifically to locate DNA binding sites within sequence data. However, comparing results amongst these various programs can be difficult. In this issue of BioTechniques, Ngoc Tam L. Tran and his colleagues at the University of Connecticut describe their development of MOTIFSIM, a web-based tool enabling comparisons and similarity detection between multiple DNA motif data sets. With MOTIFSIM, researchers can locate binding motifs that are common between different data sets and then compare the outputs of different programs. The authors tested MOTIFSIM using three experimental data sets that had each been analyzed using multiple motif finding tools. MOTIFSIM was able to identify binding sites that were in agreement with STAMP, an established program capable of performing pairwise comparisons between DNA binding motif data sets. Taken together, MOTIFSIM provides a validated tool for identifying biologically significant DNA binding motifs or discovering motifs that a single tool might have missed.See “MOTIFSIM: A web tool for detecting similarity in multiple DNA motif datasets”Can we turn back?As any molecular biologist knows, nucleic acids can be damaged by environmental or chemical conditions. Normally, living cells possess repair pathways capable of fixing such hydrolysis or oxidation damage to DNA. Once cell death occurs, however, the damage cannot be reversed naturally, creating a major challenge for researchers in the fields of ancient DNA and forensics. One strategy to overcome this problem is to incubate damaged DNA with a combination of enzymes and biomolecules to reverse post-mortem damage and therefore improve DNA quality for downstream applications. In this issue, Natalie Mouttham and her colleagues at McMaster University test this idea by examining the ability of two available repair protocols to enhance sequence data obtained from four ancient mammoth DNA samples. Unfortunately, neither protocol appreciably repaired ancient DNA samples from mammoth bones. The authors do suggest, however, that optimizing enzymatic repair will be critical for genetic analysis of heavily damaged fossil DNA extracts in the future; thus, detailed experiments and analysis, such as performed here, could provide a launching point for future studies of repair protocols.See “Surveying the repair of ancient DNA from bones via high-throughput sequencing”Affinity purification of extracellular vesiclesExtracellular vesicles (EVs) are of interest in intercellular communication and biomarker studies, but EV purification methods are still relatively nonspecific, laborious, or small-scale. Since heparin can interfere with EV uptake, Balaj et al. tested heparin affinity purification of EVs. For cell culture samples, concentrated conditioned medium is incubated with heparin-coated beads, and EVs are then eluted with high salt. Depending on the cell type, EV recovery ranges from 30%–60%. Heparin-isolated EVs have substantially less BSA contamination than ultracentrifuged samples and have higher levels of the EV-associated protein Alix. RNA profiles are comparable to those of sucrose gradient–purified EVs. Affinity-purified EVs have a normal appearance as assessed by electron microscopy and function as expected in internalization assays. For applications that don't require intact EVs, the agarose bead/high-salt elution method can be replaced by magnetic beads and kit-based RNA extraction. This convenient, high-yield method for isolating EVs from cell culture or biofluids should accelerate functional assays and biomarker identification.L. Balaj et al. 2015. Heparin affinity purification of extracellular vesicles. Sci Rep. 5:10266.Noncoding RNA tool enhances translationMove over, RNAi, there's a new RNA-based tool for regulating the expression of a target gene. RNAe (RNA enhancement) uses long noncoding RNAs containing sequences that base pair in the vicinity of a target mRNA's initiation codon and ramp up translation. Yao et al. show that RNAe works with various cell lines and targets, boosting expression up to 10-fold. Endogenous transcripts can be targeted, and RNAe may be delivered via RNAe-expressing plasmids or as in vitro–produced transcripts. For researchers preferring the latter, Yao et al. have constructed a plasmid for co-expression of a GFP- or HA-tagged target gene with an anti-tag RNAe. For those favoring a custom approach, the authors describe the minimal RNAe, which comprises a 72-nucleotide antisense sequence, a 167-nucleotide inverted SINEB2 sequence, and a poly(A) tail. RNAe should be useful for protein production and in screening assays as a complement to RNAi knockdown.Y. Yao et al. 2015. RNAe: an effective method for targeted protein translation enhancement by artificial non-coding RNA with SINEB2 repeat. Nucleic Acids Res. 43:e58.FiguresReferencesRelatedDetails Vol. 59, No. 1 Follow us on social media for the latest updates Metrics History Published online 3 April 2018 Published in print July 2015 Information© 2015 Author(s)PDF download

A multi-business company is a complex entity. Evaluating corporate performance of such an organisation is even more challenging. Corporate performance is inherently multidimensional in nature, is viewed from various perspectives, and has to satisfy multiple objectives. It is multi-dimensional in the sense of being a function of many variables that drive firm performance; multi-perspectival, from various stakeholders’ standpoints; and multi-objectives are to be optimised. So there is no single corporate performance evaluation tool that can be prescribed as a stand-alone gauge; however, a unified and holistic corporate performance management system can be developed from multiple tools. This paper attempts to bring together a variety of performance management tools that have evolved and developed in theory, and have been tested and applied in practice. In developing this convergence, first a set of criteria that answers the multi-dimensional, multi-perspectival, and multi-objective requirements of a firm’s performance will be identified and weighted. Second, management tools that have been used either singly or in combination by multi-business companies are reviewed and ranked against the chosen criteria. Finally, an integrated model or framework that brings together and unifies the elements of these ranked performance management tools is proposed. OPSOMMING ‘n Multi-maatskappy-onderneming is ‘n komplekse entiteit. Die evaluasie van maatskappyprestasie van so ‘n onderneming is self meer van ‘n uitdaging. Maatskappyprestasie is inherent multidimensioneel, word uit verskeie perspektiewe beskou en moet veelvuldige doelwitte bevredig. Geen enkele maatstaf is beskikbaar om hierdie fasette van maatskappyprestasie te evalueer, alhoewel ‘n holistiese prestasiebestuurstelsel ontwikkel kan word gebaseer op verskillende maatstawwe. Hierdie artikel bring verskillende metodologiee byeen vir die meting van maatskappyprestasie wat multi-dimensioneel, multiperspektiewelik en multi-doelwit-georienteerd is. Sodoende word ‘n geintegreerde raamwerk vir die meting van maatskappyprestasie ontwikkel.

The model accuracy of optical proximity-effect correction (OPC) was investigated by two modeling methods for a 10-nm node process. The first method is to use contours of two-dimensional structures extracted from critical dimension-scanning electron microscope (CD-SEM) images combined with conventional CDs of one-dimensional structures. The accuracy of this hybrid OPC model was compared with that of a conventional OPC model, which was created with only CD data, in terms of root-mean-square (RMS) error for metal and contact layers of 10-nm node logic devices. Results showed improvement of model accuracy with the use of hybrid OPC modeling by 23% for contact layer and 18% for metal layer, respectively. The second method is to apply a correction technique for resist shrinkage caused by CD-SEM measurement to extracted contours for improving OPC model accuracy. The accuracy of OPC model with shrink correction was compared with that without shrink correction, and total RMS error was decreased by 12% by using the shrink correction technique. It can be concluded that the use of CD-SEM contours and the shrink correction of contours are effective to improve the accuracy of OPC model for the 10-nm node process.

As minimum groundrules for chipmanufacturing continue to shrink the lithography process is pushed further and further into the low k 1 domain. One of the key characteristics of low k 1 lithography is the fact that process variations are increasingly more difficult to manage and the resulting CD variations are significant relative to the nominal dimensions. As a result it is quite common for process engineers to define process budgets, mostly dose and focus budgets. These budgets summarize the effects of various exposure and process contributors and provide the range within which the process is expected to fluctuate. An important task of process design is to ensure that within these budgets no catastrophic patterning failures occur, but even more importantly that the CD variations remain within the allowed design tolerances. Various techniques have been developed to reduce the sensitivity of the lithography process to process variations, among those one of the more prominent and quite widely adopted techniques are subresolution enhancements. Traditionally subresolution assist features are placed in the design using rules based approaches. This work presents a model based approach to assist feature placement. In this approach assist features are placed such that the resulting mask exhibits the minimum sensitivity to the specific process variations encountered. The type of process variation may be defined by the user as serious of worst case conditions, for example in dose and focus. The technique however is general enough to allow a variety of process variations to be included. This work focuses on demonstrating the key concept and show it's validity. The approach demonstrated in this work is fully integrated with the process budget concept and therefore allows a process aware mask optimization.