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Method for computing feature kernels for optical model simulation

An optical model and kernel technology, which is applied to the original parts, optics, and calculations used for optomechanical processing, can solve the problems of increasing the overall time of the mask manufacturing process, expensive calculations, and slow simulations.

Pending Publication Date: 2020-03-27
TAIWAN SEMICON MFG CO LTD
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  • Abstract
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  • Claims
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Problems solved by technology

However, standard methods for computing TCC kernels are computationally prohibitively expensive, which can slow down the simulation and increase the overall time of the mask fabrication process

Method used

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  • Method for computing feature kernels for optical model simulation
  • Method for computing feature kernels for optical model simulation
  • Method for computing feature kernels for optical model simulation

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Embodiment Construction

[0030] The following disclosure provides many different embodiments, or examples, for implementing different features of the provided subject matter. Specific examples of components and arrangements are described below to simplify the present disclosure. Of course, these are examples only and are not intended to be limiting. For example, in the following description, forming a first feature over or on a second feature may include embodiments where the first feature is formed in direct contact with the second feature, and may also include embodiments where the first feature is in direct contact with the second feature. Embodiments in which additional features may be formed between the second features such that the first and second features may not be in direct contact. In addition, the present disclosure may repeat drawing numerals and / or letters in various instances. This repetition is for simplicity and clarity and does not in itself indicate a relationship between the vari...

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Abstract

A method for computing feature kernels for optical model simulation is provided. In the method, a feature matrix mathematically describing a plurality of properties of an optical imaging system is identified. A sampling matrix comprising at least one vector serving as input to form a low-rank basis for the feature matrix is generated. The sampling matrix is iteratively multiplied by the feature matrix and a multiplication result is adaptively rescaled according to numerical stability until a convergence condition is met. The iteration results are used to form a reduced feature matrix. Decomposition values of the reduced feature matrix are computed and a plurality of feature kernels are extracted from the computed decomposition values.

Description

technical field [0001] Embodiments of the present invention relate to a method for computing a characteristic kernel, and in particular to a method for computing a characteristic kernel for optical model simulation. Background technique [0002] In the manufacturing process of modern semiconductor devices, various materials and machines are manipulated to produce the final product. Due to the increased complexity of semiconductor devices and the development of ultra-small transistors, process variations have a greater impact on product performance. Photolithography is one of the important factors that exert influence, and photolithography simulation is applied to evaluate the performance of products to be manufactured before mass production. [0003] A transmission cross-coefficient (TCC) matrix, which mathematically describes the characteristics of an optical imaging system according to Hopkins theory, is employed in the lithography simulation, and calculation of a TCC ker...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06F17/16G06F17/14G03F1/68G03F1/36
CPCG06F17/14G06F17/16G03F1/68G03F1/36G03F7/705G06F17/142G06F30/20
Inventor 何力键
Owner TAIWAN SEMICON MFG CO LTD
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