Metrology, Inspection, and Process Control for Microlithography XXXIII最新文献

{"title":"Using critical-dimension grazing-incidence small angle x-ray scattering to study line edge roughness (Conference Presentation)","authors":"G. Freychet, G. Kumar, R. Pandolfi, I. Cordova, P. Naulleau, A. Hexemer, G. Lorusso","doi":"10.1117/12.2514954","DOIUrl":"https://doi.org/10.1117/12.2514954","url":null,"abstract":"As the lithographically manufactured nanostructures are shrinking in size, conventional techniques, such as Scanning Electron Microscopes and Atomic Force Microscopes reach their resolution limits [1]. Novel inline scatterometry techniques not only provide the opportunity to bridge this gap, but they can also advance characterization of the lithographic process. The particular, Critical-Dimension Grazing incidence Small Angle X-ray Scattering (CDGISAXS) has emerged as one such promising modality to extract the profile of line gratings [2]. With the advent of brighter x-ray sources with tunable energies and faster detectors, there is a possibility for combining fast X-ray acquisition with high-speed data treatment to reach the timescale for an effective in-line characterization method.\u0000Due to recent progress in the ability to model data acquired from CD-GISAXS, we extended our model in order to study the impact of roughness. A set of twelve samples were studied. First, periodic line edge roughness (LER) and line width roughness (LWR) were measured, leading to the apparition of several semi-circle of Bragg spots as illustarted on Figure 1a. Using HIpGISAXS software, the GISAXS patterns were reproduced, allowing the extract of the periodicity of the roughness. \u0000On the second part of the line gratings, aperiodic roughness were designed with different frequencies and amplitudes. These samples led to the superposition of a semi-circle of Bragg spots with a “palm tree” feature coming from the profile of the gratings, illustarted on figure 1b. In a fist step, we extracted the in-depth profile of the gratings by fitting the modulations of the palm tree, in a similar approach as the CD-GISAXS one. In a second step, we modeled the impact of the roughness on the CD-GISAXS pattern and proposed a model to extract the roughness amplitude and frequency.\u0000\u0000References:\u0000[1] ITRS (2013). International Technology Roadmap for Semiconductors, http://www.itrs.net/. \u0000[2] Freychet, G. et al. (2018) Proc. SPIE, 10585, 1058512.\u0000[3] Freychet et al. (2018) Nanoscale Horizons, submitted. \u0000[4] Chourou, S. T., Sarje, A., Li, X. S., Chan, E. R. & Hexemer, A. (2013). J. Appl. Cryst. 46, 1781–1795.","PeriodicalId":331248,"journal":{"name":"Metrology, Inspection, and Process Control for Microlithography XXXIII","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121500666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent imaging of resists via resonant x-ray scattering: unraveling the effects of chain scission to chemical amplification (Conference Presentation)","authors":"I. Cordova, G. Freychet, S. Dhuey, A. Hexemer, P. Naulleau, Cheng Wang","doi":"10.1117/12.2515166","DOIUrl":"https://doi.org/10.1117/12.2515166","url":null,"abstract":"Even though instrumentation for electron beam lithography (EBL) has progressed immensely since it was first introduced almost 50 years ago[1], enabling beam spot sizes below 5 nm for certain systems, its lithographic resolution limits are still bound by the primary and secondary electron scattering processes that occur when a specific resist is exposed. As the feature sizes become smaller and resists designed with higher sensitivities, these stochastic processes play an increasing role in the resulting line edge roughness (LER) thus leading to an effect known as shot noise. Unfortunately, unraveling the impact of these processes from the impact of the development step is partly hindered by our inability to measure the 3D profile of the latent image from resists directly after exposure. Furthermore, given the recent rise of chemically-amplified resists (CARs) used for the next generation extreme ultraviolet lithography (EUV), it has become even more critical to find ways to characterize and investigate the shot noise effect.\u0000In this work, we tackle this challenge by applying the resonant soft x-ray scattering (RSoXS) technique in a grazing incidence configuration to extract the cross-sectional profile of resists that have already been patterned, but have yet to be developed (i.e., latent image). We find that the difference in chemistry induced by the chain scission process in exposed PMMA and CAR resists is enough to produce enough scattering contrast at certain X-ray energies near the absorption edge of carbon in order to provide a latent image profile of the pattern with sub-nanometer resolution. In this paper, we will compare the latent image profiles extracted from this RSoXS data to the profiles obtained after development, as well as expand on the nature of this chemical contrast mechanism. We will show how this scattering data may be interpreted and the information used to shed light on the nature of the resolution limit of a specific combination of resist and exposure plus development conditions. Finally, we will elaborate on the impact of the measurement itself on the resulting pattern morphology as well as how similar insights might be gained across other types of resists. \u0000\u00001. Hans C. Pfeiffer, } \"Direct write electron beam lithography: a historical overview\", Proc. SPIE 7823, Photomask Technology 2010, 782316 (24 September 2010); doi: 10.1117/12.868477","PeriodicalId":331248,"journal":{"name":"Metrology, Inspection, and Process Control for Microlithography XXXIII","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131956520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-learning-based SEM image denoiser (Conference Presentation)","authors":"D. Cerbu, S. Halder, P. Leray","doi":"10.1117/12.2515182","DOIUrl":"https://doi.org/10.1117/12.2515182","url":null,"abstract":"Deep-learning-based SEM image denoiser\u0000\u0000Dorin Cerbu1, Sandip Halder1, Philippe Leray1\u0000\u00001IMEC, Kapeldreef 75, B-3001 Leuven, Belgium\u0000\u0000\u0000We report the development of a new method to denoise SEM images with the help of artificial neural networks. Upon using a preprocessing and training scheme tailored for SEM images of structures, most often encountered in semiconductor manufacturing, we can efficiently denoise images affected with varying degrees of noise severity and origin. In the figure below, we show an example of how we can use this filter efficiently to treat noisy images and improve the image quality. This can help in acquisition of more stable and better metrology data. \u0000\u0000Fig1(a) original image (b) Image which has been denoised using deep-learning based algorithms\u0000\u0000This development is of utmost importance for the case of post-litho processing step where resist nanostructures when SEM inspected are usually impacted by the electron beam and shrink, hence skewing critical dimension measurements. This is especially true as we push towards sub N-10 nm nodes. Application of our deep-learning processing scheme allows efficient noise reduction on SEM inspection images and helps us discern minor details previously shadowed by noise. This is extremely important as we move towards using EUV in high volume manufacturing. Small details can be crucial to understand the root-cause of stochastic and process defects. In previous work, we have already shown different approaches to understand stochastic defects [1-2]. The goal of this work is to enhance the image quality as much as possible to gain further fundamental understanding on nano-defects. \u0000\u0000[1] S. Halder et. al., ‘Using machine learning techniques to understand EUV stochastics, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018\u0000[2] K. Sah et.al., ‘EUV stochastic defect monitoring with advanced Broadband optical wafer inspection and e-Beam review systems’, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018","PeriodicalId":331248,"journal":{"name":"Metrology, Inspection, and Process Control for Microlithography XXXIII","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115381213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress on sub-wavelength nanoimaging with a coherent tabletop EUV source (Conference Presentation)","authors":"C. Bevis, Robert M. Karl, Bin Wang, Peter C Johnsen, M. Tanksalvala, Christina L. Porter, Yuka Esashi, H. Kapteyn","doi":"10.1117/12.2517026","DOIUrl":"https://doi.org/10.1117/12.2517026","url":null,"abstract":"","PeriodicalId":331248,"journal":{"name":"Metrology, Inspection, and Process Control for Microlithography XXXIII","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114443562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}