View source for 18350611. LITHOGRAPHY MODEL SIMULATION METHOD, PHOTOMASK GENERATING METHOD USING THE SAME, AND SEMICONDUCTOR DEVICE FABRICATION METHOD USING THE SAME simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

=LITHOGRAPHY MODEL SIMULATION METHOD, PHOTOMASK GENERATING METHOD USING THE SAME, AND SEMICONDUCTOR DEVICE FABRICATION METHOD USING THE SAME=


==Organization Name==

[[:Category:SAMSUNG ELECTRONICS CO., LTD.|SAMSUNG ELECTRONICS CO., LTD.]]


[[Category:SAMSUNG ELECTRONICS CO., LTD.]]

==Inventor(s)==

[[:Category:Han Veen Koh of Suwon-si (KR)|Han Veen Koh of Suwon-si (KR)]][[Category:Han Veen Koh of Suwon-si (KR)]]

[[:Category:Soo Yong Lee of Suwon-si (KR)|Soo Yong Lee of Suwon-si (KR)]][[Category:Soo Yong Lee of Suwon-si (KR)]]

[[:Category:Moo-Joon Shin of Suwon-si (KR)|Moo-Joon Shin of Suwon-si (KR)]][[Category:Moo-Joon Shin of Suwon-si (KR)]]

[[:Category:Kyoung Yoon Park of Suwon-si (KR)|Kyoung Yoon Park of Suwon-si (KR)]][[Category:Kyoung Yoon Park of Suwon-si (KR)]]

==LITHOGRAPHY MODEL SIMULATION METHOD, PHOTOMASK GENERATING METHOD USING THE SAME, AND SEMICONDUCTOR DEVICE FABRICATION METHOD USING THE SAME - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18350611 titled 'LITHOGRAPHY MODEL SIMULATION METHOD, PHOTOMASK GENERATING METHOD USING THE SAME, AND SEMICONDUCTOR DEVICE FABRICATION METHOD USING THE SAME

==Simplified Explanation==

The patent application describes a method for simulating lithography models in order to generate a resist image. The method involves several steps, including receiving a first mask image, generating a second mask image by simulating an optical model on the first mask image, generating at least one third mask image by simulating a quenching model on the second mask image, and generating a resist image by performing machine learning on the first, second, and third mask images. The resist image is generated by convolving each mask image with a corresponding kernel and adding the output data together. The kernels used in the process are free-form kernels.

* The method simulates lithography models to generate a resist image.
* It involves receiving a first mask image and generating a second mask image using an optical model.
* At least one third mask image is generated using a quenching model.
* Machine learning is performed on the first, second, and third mask images to generate the resist image.
* The resist image is generated by convolving each mask image with a corresponding kernel and adding the output data together.
* The kernels used in the process are free-form kernels.

Potential applications of this technology:

* Semiconductor manufacturing: The method can be used in the fabrication of semiconductor devices, where lithography plays a crucial role in defining patterns on the semiconductor material.
* Optics industry: The method can be applied in the production of optical components, such as lenses and mirrors, where precise patterning is required.
* Nanotechnology: The method can be utilized in nanofabrication processes, enabling the creation of intricate nanostructures.

Problems solved by this technology:

* Accurate simulation: The method allows for the simulation of lithography models, which helps in predicting and optimizing the resist image formation process.
* Mask optimization: By simulating different mask images, the method aids in optimizing the design and performance of masks used in lithography.
* Quenching model simulation: The method provides a means to simulate the quenching effect, which is important in accurately predicting the resist image formation.

Benefits of this technology:

* Improved lithography process: The method enables the generation of a resist image with higher accuracy and quality, leading to improved overall lithography process.
* Time and cost savings: By simulating different mask images and performing machine learning, the method reduces the need for physical trial and error, saving time and resources.
* Enhanced design optimization: The ability to simulate and analyze various mask images helps in optimizing the design and performance of masks, leading to improved patterning results.


==Original Abstract Submitted==

Provided is a lithography model simulation method. The method comprises receiving a first mask image, generating a second mask image by simulating an optical model on the first mask image, generating at least one third mask image by simulating a quenching model on the second mask image, and generating a resist image by performing machine learning on the first mask image, the second mask image, and the third mask image. The generating of the resist image comprises outputting first output data by convolving the first mask image with a first kernel, outputting second output data by convolving the second mask image with a second kernel, outputting third output data by convolving the third mask image with a third kernel, and adding together the first to third output data. Each of the first to third kernels is or includes a free-form kernel.



[[Category:G03F7/00]]