WeChat Mini Program
Old Version Features

    • Predictor of Thermal Aberrations Via Particle Filter for Online Compensation

    Yanjie Mao,Sikun Li,Gang Sun,Lifeng Duan,Weijie Shi,Yang Bu,Xiangzhao Wang

    Journal of Micro/Nanolithography MEMS and MOEMS（2019）

    Chinese Acad Sci

    Cited 0|Views29
    Abstract
    In optical lithography, aberrations induced by lens heating effects of a projection lens lead to degradation of imaging quality. In order to compensate for thermal aberrations, it is crucial to apply an accurate method for thermal aberration prediction. An effective and accurate method for thermal aberration prediction is proposed. A double exponential model is modified in respect of the timing of exposure tools, and a particle filter is used to adjust the double exponential model. Parameters of the model are updated recursively pursuant to the aberration data measured during the exchange of wafers. The updated model is used to predict thermal aberrations during the following exposure of wafers. The performance of the algorithm is evaluated by the simulation of a projection lens for argon fluoride lithography. Simulation results show that predictive errors of primary defocus and astigmatism are significantly reduced, and the mean value of wavefront error in the whole field of view is reduced by about 30% in a vertical line/space pattern. The proposed method is easily adaptable to different types of aberration measurement error. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
    More
    Translated text
    Key words
    lithography,lens heating effect,thermal aberration,particle filter
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined