Comparison of simulation approaches for chemically amplified resists

Modeling has become indespensable tool for chemically amplified resist (CAR) evaluations. It has been used extensively to study acid diffusion and its effects on resist image formation. Several commercial and academic simulators have been developed for CAR process simulation. For commercial simulators such as PROLITH (Finle Technologies) and Solid-C (Sigma-C), the user is allowed to choose between an empirical model or a concentration dependant diffusion model. The empirical model is faster but not very accurate for 2-dimension resist simulations. In this case there is a trade off between the speed of the simulator and the accuracy of the results. An academic simulator such as STORM (U.C. Berkeley) gives the user a choice of different algorithms including Fast Imaging 2nd order finite difference algorithm and Moving Boundary finite element algorithm. A user interested in simulating the volume shrinkage and polymer stress effects during post exposure bake will need the Moving Boundary algorithm whereas a user interested in the latent image formation without polymer deformations will find the Fast Imaging algorithm more appropriate. The Fast Imaging algorithm is generally faster and requires less computer memory. This choice of algorithm presents a trade off between speed and level of detail in resist profile prediction. This paper surveys the different models and simulator algorithms available in the literature. Contributions in the field of CAR modeling including contributions to characterization of CAR exposure and post exposure bake (PEB) processes for different resist systems. Several numerical algorithms and their performances will also be discussed in this paper.

Progress towards 65 nm next-generation lithography requires unprecedented global CD uniformity, with the actual ITRS 2002 roadmap proposing 4.2 nm 3/spl sigma/ (dense lines) for 65 nm binary masks. Since resolution requirements are satisfied only by using chemically amplified resists (CARs), exposure and post-exposure bake (PEB) are key processes to successful mask making, both introducing global CD errors. Develop and etch processes potentially contribute further global CD errors. The global CD uniformity can be improved significantly by adaptive PEB, especially for CARs showing moderate to strong PEB sensitivity, like NEB22. With the 25-zone hotplate of the APB5500 bake system, facilitated through a novel calibration mask with 25 equidistant temperature sensors within the resist plane, an appropriate temperature profile can be applied during PEB. This temperature profile is automatically calculated by an adaptive optimization algorithm, based on 2-dimensional spline fitting of a CD measurement. A CD-uniformity improvement (dense lines) from 4.8 nm 3/spl sigma/ to 3.9 nm 3/spl sigma/ (/spl cong/20%) on a state-of-the-art production mask is achieved for the chrome layer (ASI, after strip inspection).

Characterization for the photomask fabrication based on a high-resolution technique with a non-chemically amplified resist and a post-exposure bake

Prevalence of fungal involvement in a series of patients with nail psoriasis

Post Exposure Baking (PEB) has been widely utilized in advanced lithography. PEB simulation is critical in the lithography simulation flow, as it bridges the optical simulation result and the final developed profile in the photoresist. The process of PEB can be described by coupled partial differential equations (PDE) and corresponding boundary and initial conditions. Recent years have witnessed growing presence of machine learning algorithms in lithography simulation, while PEB simulation is often ignored or treated with compact models, considering the huge cost of solving PDEs exactly. In this work, based on the observation of the physical essence of PEB, we propose DeePEB: a neural PDE Solver for PEB simulation. This model is capable of predicting the PEB latent image with high accuracy and >100 × acceleration (compared to the commercial rigorous simulation tool), paving the way for efficient and accurate photoresist modeling in lithography simulation and layout optimization.

Several types of chemically amplified resists (CARs) have been evaluated in comparison with other types of resists. The evaluated CARs were found to have better performance than conventional resists and to satisfy the requirements for next generation reticle fabrication. Especially CARs have high sensitivity and high contrast enough to make photomasks with e-beam writers and have good dry-etch durability. We evaluated the allowance of baking conditions. It was found that it was important to minimize the dependence on prebake temperature as well as on post exposure bake temperature. The influence of airborne contaminants on post exposure delay (PED) stability of CARs was investigated. PED stability depends on resist materials and the ammonia concentration in the process environment. The use of a chemical filter is effective in improving PED stability. In conclusion, CARs can meet the requirements for resists used for next generation reticle fabrication.

By combining electrical linewidth measurements and neural-network (NN) process metamodeling, lithography simulators can be calibrated in an efficient way. In this work we present a novel methodology for characterizing postexposure bake using a very large experimental data set, so that the calibrated model can be used as a truly predictive tool. The adoption of a special test reticle mask allowed us to collect more than 700 000 critical dimensions CDs from 24 silicon wafers for a matrix of postexposure bake (PEB) time, and temperature conditions. The lithographic patterns included isolated, semidense and dense lines for structures of 0.25, 0.20, 0.175, and 0.15 μm nominal size replicated across the exposure field and across the wafer. As a result of this particular metrology, each measured CD was associated with both topological (position on the wafer and position within the field) and process information (exposure dose, PEB time, and temperature). Database management techniques were implemented in order to extract and analyze such a massive data set. Process metamodeling (PMM) was used for the calibration of a PEB model describing the joint effect of photoacid diffusion and photoacid loss, coupled with a deprotection reaction. PMM creates a NN model of the PEB original model (a “model of a model”) so that the diffusion coefficient, the acid loss, and the deprotection rates can be estimated by inversion of the NN mapping. The comparisons between experimental and simulated data show excellent agreement that is maintained across the entire process space.

Chemically amplified resists (CARs) are the current workhorse for photolithography, where higher resolution and smaller feature size represent a continual driving force for the semiconductor industry. As the feature size decreases to sub-30 nm, LWR and gate critical dimension (CD) control become serious concerns. In order to reach the goals in the ITRS, an unprecedented level of control of photoacid diffusion while maintaining the high resist sensitivity and resolution during image formation is required. CARs require a post exposure bake (PEB), typically performed on a hot plate at 90-150°C for 30-120 seconds, to complete the resist deprotection after photoacid generation. This bake step is a primary influence on resist performance as the time/temperature profile controls both the diffusion of photogenerated acids and the deprotection of the resist backbone. Sufficient time must be provided to achieve the level of deprotection required for the solubility switching in a developer, but the seconds timeframe of conventional hot plate PEB leads to an undesirable amount of acid diffusion. As long as the activation energy of diffusion is less than that for deprotection, higher temperatures for optimized time durations will result in reduced diffusion. However, traditional hot plate PEB cannot access times shorter than a few seconds. We utilize a laser (CO₂) based scanned heating system to achieve sub-millisecond to milliseconds in heating durations with temperatures up to the thermal decomposition limit of the resist. This research is aimed at using synthetic techniques to vary the structure of the photoacid generator (PAG) in order to learn about the role of PAG size and structure on acid diffusion during sub-millisecond heating. A variety of PAGs with different anion sizes have been synthesized and tested on the CO₂ laser system, and their lithographic performance and effect on acid diffusion has been studied.

The second level exposure of Alternating Phase Shift Mask has to be accomplished by e-beam lithography. However, e-beam writing on structured open chrome layers may induce severe charging and yields to deteriorate overlay accuracy. In case of charging problems, applying of conductive top coats on resists help to reduce the distortion of the second layer. ESPACER makes the conductive layer on the e-beam resist and prevent a positioning error during e-beam writing by its shield effect. However, ESPACER required removal of its topcoat before post-exposure bake (PEB) because of the undesirable effect to the resist, due to the acid diffusion from the ESPACER film to high sensitive Chemical Amplified Resists (CARs). Our investigations were focused on the combination of the representative positive tone CAR: FUJIFILM ELECTRONIC MATERIALS CO., LTD and the new-type ESPACER called ESPACER 300F. ESPACER 300F was possible to use the PEB process before its removal, improved by exchanging a lower mobility surfactant than before. This material also had a good wettability, a low resist-thickness losses and kept good pattern shapes of the resist. Additionally, ESPACER 300F have little influence in both overlay accuracy at the variety of pattern density and sensitivity of the resist, therefore it would be the good material for making reticles for 65nm node and beyond.

Journal of Polymer ScienceVolume 46, Issue 147 p. 268-270 Letters to the Editor Differentiation of stereoregular polymethyl methacrylates by the film-balance technique Nicky Beredjick, Nicky Beredjick Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this authorR. A. Ahlbeck, R. A. Ahlbeck Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this authorT. K. Kwei, T. K. Kwei Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this authorHerman E. Ries Jr., Herman E. Ries Jr. Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this author Nicky Beredjick, Nicky Beredjick Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this authorR. A. Ahlbeck, R. A. Ahlbeck Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this authorT. K. Kwei, T. K. Kwei Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this authorHerman E. Ries Jr., Herman E. Ries Jr. Research and Development Department, Standard Oil Company (Indiana), Whiting, IndianaSearch for more papers by this author First published: September 1960 https://doi.org/10.1002/pol.1960.1204614728Citations: 33AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume46, Issue147September 1960Pages 268-270 RelatedInformation

Journal of High Resolution ChromatographyVolume 13, Issue 6 p. 445-446 Short Communication Detecation and quantitation of potential impurities in clonixin by GC/MS M. H. Esteve, Corresponding Author M. H. Esteve Research and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaResearch and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaSearch for more papers by this authorR. D. Rofi, R. D. Rofi Research and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaSearch for more papers by this authorG. E. Aldomá, G. E. Aldomá Research and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaSearch for more papers by this author M. H. Esteve, Corresponding Author M. H. Esteve Research and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaResearch and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaSearch for more papers by this authorR. D. Rofi, R. D. Rofi Research and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaSearch for more papers by this authorG. E. Aldomá, G. E. Aldomá Research and Development Department, LAPLEX S.A., Avenida E. Garzón 5928, 1440-Buenos Aires, ArgentinaSearch for more papers by this author First published: June 1990 https://doi.org/10.1002/jhrc.1240130616Citations: 2AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume13, Issue6June 1990Pages 445-446 RelatedInformation

Environmental MutagenesisVolume 7, Issue S4 p. 3-7 Article Introduction to the genetic toxicology association workshop on statistical analysis of cytogenetics test systems Dr. Joseph D. Irr, Corresponding Author Dr. Joseph D. Irr Central Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DelawareCentral Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DE 19898===Search for more papers by this authorRonald D. Snee, Ronald D. Snee Central Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DelawareSearch for more papers by this authorG. Jay Graepel, G. Jay Graepel Central Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DelawareSearch for more papers by this author Dr. Joseph D. Irr, Corresponding Author Dr. Joseph D. Irr Central Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DelawareCentral Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DE 19898===Search for more papers by this authorRonald D. Snee, Ronald D. Snee Central Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DelawareSearch for more papers by this authorG. Jay Graepel, G. Jay Graepel Central Research and Development Department, E.I. du Pont de Nemours & Co., Inc., Wilmington, DelawareSearch for more papers by this author First published: 1985 https://doi.org/10.1002/em.2860070803Citations: 2AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume7, IssueS41985Pages 3-7 RelatedInformation

Extreme ultraviolet (EUV) lithography with 13.5 nm wavelength is the main option for sub-10nm patterning in the semiconductor industry. We report improvements in resist performance towards EUV high volume manufacturing. A local CD uniformity (LCDU) model is introduced and validated with experimental contact hole (CH) data. Resist performance is analyzed in terms of ultimate printing resolution (R), line width roughness (LWR), sensitivity (S), exposure latitude (EL) and depth of focus (DOF). Resist performance of dense lines at 13 nm half-pitch and beyond is shown by chemical amplified resist (CAR) and non-CAR (Inpria YA Series) on NXE scanner. Resolution down to 10nm half pitch (hp) is shown by Inpria YA Series resist exposed on interference lithography at the Paul Sherrer Institute. Contact holes contrast and consequent LCDU improvement is achieved on a NXE:3400 scanner by decreasing the pupil fill ratio. State-of-the-art imaging meets 5nm node requirements for CHs. A dynamic gas lock (DGL) membrane is introduced between projection optics box (POB) and wafer stage. The DGL membrane will suppress the negative impact of resist outgassing on the projection optics by 100%, enabling a wider range of resist materials to be used. The validated LCDU model indicates that the imaging requirements of the 3nm node can be met with single exposure using a high-NA EUV scanner. The current status, trends, and potential roadblocks for EUV resists are discussed. Our results mark the progress and the improvement points in EUV resist materials to support EUV ecosystem.

CONTENTS 31.1 Introduction into Lithography Simulation 65031.1.1 Aerial and Resist Image Formation 651 31.1.2 Physical/Chemical Modifications during the Processing of thePhotoresist 654 31.1.3 General Simulation Strategies 65531.2 Rigorous Modeling of Light Diffraction from Masks 656 31.2.1 Motivation 656 31.2.2 General Remarks on Rigorous Electromagnetic Field Solvers 658 31.2.3 Finite-Difference Time-Domain Method 66031.2.3.1 A Short History of FDTD 660 31.2.3.2 The FDTD Algorithm 66031.2.4 Coupling with Standard Lithography Simulations and Performance Considerations 66731.2.5 Field Decomposition Techniques and other Enhancements 669 31.3 Application of Simulation to Different Mask Types 67031.3.1 Binary Masks 670 31.3.2 Phase Shift Masks 672 31.3.3 EUV-Masks 67531.3.3.1 Efficient Modeling of EUV-Masks 676 31.3.3.2 Mask-Induced Imaging Artifacts in EUV Lithography 67931.4 Simulation of Mask Defects 681 31.4.1 Standard Defect Printability Considerations 681 31.4.2 Rigorous EMF Simulation of Defects on Optical Masks 683 31.4.3 EUV Multilayer Defects 68531.5 Summary 686 Acknowledgments 687 References 688Modeling and simulation have become indispensable tools for the understanding and optimization of lithographic processes and for the development of new process technology. Aerial image simulation is used to evaluate the imaging of designed photomasks and to explore the impact of optical parameters, such as numerical aperture (NA), partial coherence s, and defocus, and wave aberrations on the imaging performance of a projection stepper or scanner. Other simulation approaches are used to describe theimpact of the photoresist thickness, of the post-exposure bake (PEB) temperature and of the development characteristics of the photoresist on the total process performance. Lithography simulation is also used as an effective learning tool for new process engineers, scientists, and managers.

Event Abstract Back to Event Stochastic modeling of neuronal signaling Jukka Intosalmi1*, Tiina Manninen1, Keijo Ruohonen1 and Marja-Leena Linne1 1 Tampere University of Technology, Finland The time evolution of biochemical systems in neurons is traditionally modeled using deterministic ordinary differential equations (ODEs). Chemical reactions, however, are random in nature, and the deterministic approach is valid only for a restricted class of systems. Stochastic models take random fluctuations into account and are thus more realistic. Biochemical reactions can be modeled stochastically using numerous different methods. An ideal model would have the following three important properties. First, the model should be as realistic as possible, second, the mathematical method should be easily implementable as a computer algorithm, and third, the algorithm should be computationally effective. Naturally, all these three conditions cannot be fulfilled at the same time. Some realistic modeling approaches can be derived directly from chemical kinetics without making any approximations. Such approaches are called exact. A good example of an exact modeling approach is the stochastic simulation algorithm (SSA) developed by Gillespie. The SSA is applicable when the molecular populations in the system are small, but it becomes computationally inefficient when the numbers of molecules increase. In order to construct stochastic models that can be effectively simulated, new mathematical approaches have to be explored. As an approximate method also stochastic differential equations (SDEs) have been considered a promising way to model biochemical reactions stochastically. The SDE approach is attractive especially if we consider a system for which the SSA is computationally inefficient and the traditional deterministic ODE approach cannot be used as a good approximation. SDE models treat the chemical populations as real numbers and the construction of the model is based on the law of mass action, similarly as the construction of the traditional deterministic modeling approach. The SDE model, however, takes the random fluctuations into account by describing the time evolution of the system with the stochastic Itô process instead of the deterministic set of ODEs. The Itô process is basically a set of coupled stochastic differential equations. Although SDE modeling usually leads to equations that cannot be solved analytically, the solutions can be approximated numerically using different numerical integration methods. Thus, SDEs provide a mathematically rigorous way to construct models that can be simulated effectively. In order to investigate how well the SDE approach models biochemical systems, the results have to be compared with experimental data or with the simulation results from some exact simulation procedure. In this study, we use the methodology of spectral analysis to compare the simulation results of the SDE model and the SSA. As case studies, we consider e.g. calcium binding and protein kinase C (PKC) signal transduction pathway. We investigate the nature of noise in different modeling approaches and try to interpret the meaning of the noise in real biological systems. The main goal of our study is to find the most essential components and parameters in the SDE model and to adjust them so that the model is capable of giving similar results as the SSA. The simulations are carried out using the distributed computing resources (GRID) provided by Techila Technologies Ltd. Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Poster Presentation Topic: Computational Neuroscience Citation: Intosalmi J, Manninen T, Ruohonen K and Linne M (2008). Stochastic modeling of neuronal signaling. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.023 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: Jukka Intosalmi, Tampere University of Technology, Tampere, Finland, jukka.intosalmi@tut.fi Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jukka Intosalmi Tiina Manninen Keijo Ruohonen Marja-Leena Linne Google Jukka Intosalmi Tiina Manninen Keijo Ruohonen Marja-Leena Linne Google Scholar Jukka Intosalmi Tiina Manninen Keijo Ruohonen Marja-Leena Linne PubMed Jukka Intosalmi Tiina Manninen Keijo Ruohonen Marja-Leena Linne Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.