Dr. Cheng-En R. Wu Profile

Dr. Cheng-En R. Wu

at Synopsys Taiwan Co Ltd

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Author

Publications (10)

Proceedings Article | 13 June 2022 Presentation

Improved prediction of assist feature printing with physically-based NTD compact models

Folarin Latinwo, Yulu Chen, Cheng-En Wu, Peter Brooker, Hyesook Hong, Delian Yang, Hua Song, Kevin Lucas

Proceedings Volume 12052, 120520P (2022) https://doi.org/10.1117/12.2615990

KEYWORDS: Printing, Data modeling, Data processing, Semiconductors, Semiconducting wafers, Process control, Photoresist materials, Photoresist developing, Optical lithography, Metrology

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Proceedings Article | 13 June 2022 Presentation

Feasibility study of machine learning modeling for full chip optical proximity correction

Yuping Cui, Jing Xue, Jing Sha, Zheng Guo Chen, Larry Zhuang, Derren Dunn, Jeffrery Shearer, Li-Jin Chen, Rich Wu, Charlie King

Proceedings Volume 12052, 120520R (2022) https://doi.org/10.1117/12.2614060

KEYWORDS: Optical proximity correction, Machine learning, Data modeling, Neural networks, Process modeling, Photomasks, Wafer-level optics, Semiconducting wafers, Lithography, Photoresist processing

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Proceedings Article | 22 February 2021 Presentation

Validation of machine learning OPC compact models for advanced manufacturing

Moongyu Jeong, Marco Guajardo, Cheng-En Wu, Song-haeng Lee, Tim Fuehner, Lena Zavyalova, Li-Jin Chen, Hua Song, Kevin Lucas

Proceedings Volume 11614, 116140R (2021) https://doi.org/10.1117/12.2584940

KEYWORDS: Optical proximity correction, Machine learning, Data modeling, Manufacturing, Critical dimension metrology, Semiconductors, Photomasks, Semiconducting wafers, Optical lithography, Neural networks

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Proceedings Article | 21 March 2019 Presentation + Paper

Improved validation and optimization of physics-based NTD compact modeling flows

Folarin Latinwo, Delian Yang, Cheng-En (Rich) Wu, Peter Brooker, Yulu Chen, Hua Song, Kevin Lucas

Proceedings Volume 10961, 109610G (2019) https://doi.org/10.1117/12.2516344

KEYWORDS: Data modeling, Calibration, 3D modeling, Photoresist developing, Photoresist materials, Lithography, Optical lithography

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Due to the semiconductor industry’s ever increasing need for finer resolution and improved critical dimension (CD) control, negative tone development (NTD) photoresists (resists) have been adopted for several advanced applications in lithographic patterning. NTD resists enable brightfield imaging by using an organic solvent developer to penetrate and remove the unexposed regions of the resist [1]. For certain critical patterning layers, such as metal trenches and vias, NTD resists are able to provide better resist imaging quality compared to the previous positive tone development (PTD) resist process. However, there are several additional engineering difficulties which must be addressed for an NTD resist process. Specifically, NTD resists have low contrast organic solvent development and in an NTD process the material remaining on the wafer substrate is exposed resist which has been substantially transformed both chemically and mechanically. Therefore, the remaining exposed resist shows significantly more complex physical behavior than the remaining PTD resist and these behaviors require substantial improvement in an OPC (compact) model’s physical modeling accuracy in order to match wafer data and trends [2,3]. Additionally, these more complex resist behaviors place further requirements on the physical validation of OPC modeling inference. In this paper, we present results of our work to understand and improve the optimization and physical validation of physics-based NTD compact modeling flows by utilizing new methods for analysis and automation. We utilize a complete compact model flow containing physics-based resist model forms for chemically amplified resist (CAR) exposure, CAR reaction-diffusion, resist top-loss due to exposure combined with post-exposure bake (PEB), low contrast organic solvent development of resist, and mechanical deformation effects in multiple process steps. We present solid evidence that this physically-based flow has been validated for accuracy and predictability by comparing it to several experimental NTD datasets and to results of rigorous 3D lithography simulation models which were trained to fit other experimental NTD data. We additionally compared key physics-based model forms from the compact model to the more complex full time-based moving surface NTD models of the rigorous 3D simulation. We next analyzed the key physics-based compact model forms for sensitivity to input testpattern type, layout and mask dimension (e.g., linearity and MEEF), traditional dose-focus variations, as well as systematic and random noise in CD metrology. We present the results of this study and make recommendations for minimum testpattern and overall process space data to include in NTD compact model datasets. We also present flow benefits obtained from automating different validation tests including the usefulness of employing rigorous lithography simulation NTD results early in the compact modeling flow to improve overall model quality. [1] S-H. Lee, et all. Understanding dissolution behavior of 193-nm photoresists in organic solvent developers.

Proceedings Article | 20 March 2019 Paper

Investigation of machine learning for dual OPC and assist feature printing optimization

Marco Guajardo, Yulu Chen, Peter Brooker, C.-E. Wu, Kevin Hooker, Folarin Latinwo, Kevin Lucas

Proceedings Volume 10962, 109620E (2019) https://doi.org/10.1117/12.2516134

KEYWORDS: Printing, Optical proximity correction, Photomasks, Calibration, 3D modeling, Semiconducting wafers, Machine learning, Data modeling, Lithography

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Proceedings Article | 8 October 2018 Presentation + Paper

3D NTD resist deformation compact model for OPC and ILT applications

Rich Wu, Delian Yang, Folarin Latinwo, Kevin Lucas, Hua Song

Proceedings Volume 10810, 108101A (2018) https://doi.org/10.1117/12.2501992

KEYWORDS: 3D modeling, Photoresist processing, Optical proximity correction, Photoresist materials, Photomasks, Optical lithography, Photoresist developing, Semiconducting wafers, Lithography, Data modeling

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Proceedings Article | 18 March 2015 Paper

Photoresist 3D profile related etch process simulation and its application to full chip etch compact modeling

Cheng-En Wu, Wayne Yang, Lan Luan, Hua Song

Proceedings Volume 9426, 94261Q (2015) https://doi.org/10.1117/12.2086048

KEYWORDS: Etching, Ions, Plasma, Data modeling, Plasma etching, Anisotropy, Reflection, Critical dimension metrology, 3D modeling, Monte Carlo methods

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The optical proximity correction (OPC) model and post-OPC verification that takes the developed photoresist (PR) 3D profile into account is needed in the advanced 2Xnm node. The etch process hotspots caused by poor resist profile may not be fully identified during the lithography inspection but will only be observed after the subsequent etch process. A complete mask correction that targets to final etch CD requires not only a lithography R3D profile model but also a etch process compact model. The drawback of existing etch model is to treat the etch CD bias as a function of visibility and pattern density which do not contain the information of resist profile. One important factor to affect the etch CD is the PR lateral erosion during the etch process due to non-vertical PR side wall angle (SWA) and anisotropy of etch plasma source. A simple example is in transferring patterns from PR layer to thin hard mask (HM) layer, which is frequently used in the double pattern (DPT) process. The PR lateral erosion contributes an extra HM etch CD bias which is deviated from PR CD defined by lithography process. This CD bias is found to have a nontrivial dependency on the PR profile and cannot be described by the pattern density or visibility. In this report, we study the etch CD variation to resist SWA under various etch conditions. Physical effects during etch process such as plasma ion reflection and source anisotropy, which modify the local etch rate, are taken into considerations in simulation. The virtual data are generated by Synopsys TCAD tool Sentaurus Topography 3D using Monte Carlo engine. A simple geometry compact model is applied first to explain the behavior of virtual data, however, it works to some extent but lacks accuracy when plasma ion reflection comes into play. A modified version is proposed, for the first time, by including the effects of plasma ion reflection and source anisotropy. The new compact model fits the nonlinear etch CD bias very well for a wide range of resist SWAs from 65 to 90 degrees, which covers the resist profile diversities in most real situations. This result offers a potential application for both resist profile aware and etch process aware mask correction model in the mask synthesis flow.

Proceedings Article | 18 March 2015 Paper

Low-contrast photoresist development model for OPC application at 10nm node

Cheng-En Wu, David Wei, Charlie Zhang, Hua Song

Proceedings Volume 9426, 94260N (2015) https://doi.org/10.1117/12.2086044

KEYWORDS: Data modeling, Optical proximity correction, Photoresist developing, Photoresist materials, Critical dimension metrology, Photoresist processing, Lithography, Semiconducting wafers, Physics, Reverse modeling

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The Optical Proximity Correction (OPC) model, a key to process yield in the mask synthesis flow, is getting more and more complicated and challenging at advanced technology nodes (1X nm). To achieve accurate critical dimension (CD) prediction and model robustness on varied designed patterns, a rigorously tuned compact model (RTCM) [1] that takes the photoresist chemical effects into considerations is strongly desired. A lithography process consists of three main stages: Exposure, Post-Exposure Bake (PEB), and Photoresist Development. Each stage is characterized by its fundamental physics or chemistry that governs the process of illumination induced photo-acid generation, thermally activated chemical reaction-diffusion, and developer dependent photoresist dissolution, respectively. The final resist profile is determined by the process details of all these stages directly or indirectly. For an ideal resist that the development contrast approaches infinity, resist development is aptly represented by a threshold model applied to the PEB latent image (acid or inhibitor concentration). So the quality of OPC modeling is largely determined by the fidelity of PEB latent image [2,3]. However, for some types of resist and developer used in Negative Tone Development (NTD), the development contrast shows a long tail without a sharp transition. For such low-contrast resist, the developed resist profile is no longer described well by the equilevel surface of PEB latent image. Going beyond the threshold approximation, we start from the fundamental equations of resist development physics and analyze the time evolution of development front that determines the resist profile. In this paper, a new compact model is derived to catch the main physics in resist Development, which is also simple and computationally efficient to suit for OPC applications. Comparison with S-LITHO rigorous solutions and real-wafer experiments with 1D and 2D test patterns have showed that the new compact model, with fewer free parameters, provides better CD prediction than the existing empirical lumped parameter models for low-contrast resists. The new physical compact model offers a more accurate and extendable solution for OPC modeling at the 10nm node and onward.

Proceedings Article | 31 March 2014 Paper

Improving 3D resist profile compact modeling by exploiting 3D resist physical mechanisms

Yongfa Fan, Cheng-En Wu, Qian Ren, Hua Song, Thomas Schmoeller

Proceedings Volume 9052, 90520X (2014) https://doi.org/10.1117/12.2048999

KEYWORDS: 3D modeling, Diffusion, Calibration, Data modeling, Lithography, Photoresist processing, Optical proximity correction, Etching, Lithographic illumination, Semiconducting wafers

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3D Resist profile aware OPC has becoming increasingly important to address hot spots generated at etch processes due to the mass occurrence of non-ideal resist profile in 28nm technology node and beyond. It is therefore critical to build compact models capable of 3D simulation for OPC applications. A straightforward and simple approach is to build individual 2D models at different image depths either based on actual wafer measurement data or virtual simulation data from rigorous lithography simulators. Individual models at interested heights can be used by downstream OPC/LRC tools to account for 3D resist profile effects. However, the relevant image depths need be predetermined due to the discontinuous nature of the methodology itself. Furthermore, the physical commonality among the individual 2D models may deviate from each other as well during the separate calibration processes. To overcome the drawbacks, efforts are made in this paper to compute the whole bulk image using Hopkins equation in one shot. The bulk image is then used to build 3D resist models. This approach also opens the feasibility of including resist interface effects (for example, top or bottom out-diffusion), which are important to resist profile formation, into a compact 3D resist model. The interface effects calculations are merged into the bulk image Hopkins equation. Simulation experiments are conducted to demonstrate that resist profile heavily rely on interface conditions. Our experimental results show that those interface effects can be accurately simulated with reference to rigorous simulation results. In modeling reality, such a 3D resist model can be calibrated with data from discrete image planes but can be used at arbitrary interpolated planes. One obvious advantage of this 3D resist model approach is that the 3D model is more physically represented by a common set of resist parameters (in contrast to the individual model approach) for 3D resist profile simulation. A full model calibration test is conducted on a virtual lithography process. It is demonstrated that 3D resist profile of the process can be precisely captured by this method. It is shown that the resist model can be carried to a different lithography process with same resist setup but a different illumination source without model any accuracy degradation. In an additional test, the model is used to demonstrate the capability of resist 3D profile correction by ILT.

Proceedings Article | 16 September 2013 Paper

AF printability check with a full-chip 3D resist profile model

Cheng-En Wu, Jason Chang, Hua Song, James Shiely

Proceedings Volume 8880, 88802D (2013) https://doi.org/10.1117/12.2027843

KEYWORDS: Atrial fibrillation, 3D modeling, Calibration, Data modeling, Printing, Optical proximity correction, Diffusion, Semiconducting wafers, Photoresist materials, Etching

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