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Deep learning method for computational lithography

A deep learning and computational lithography technology, applied in optics, microlithography exposure equipment, optomechanical equipment, etc., can solve problems such as limiting application scope, avoid training errors or training failures, reduce computational complexity, and simplify training The effect of the process

Active Publication Date: 2019-08-30
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, most practical lithography systems use partially coherent illumination, thus limiting the scope of application of the above techniques

Method used

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  • Deep learning method for computational lithography
  • Deep learning method for computational lithography
  • Deep learning method for computational lithography

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Embodiment Construction

[0032] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0033] Please refer to figure 1 , figure 1 A deep learning method for computational lithography provided by an embodiment of the present invention is shown, and its basic scheme is:

[0034] S1. Based on the lithographic imaging model, construct the mask graphic update formula in the gradient iterative algorithm; expand the iterative process of the gradient iterative algorithm, intercept the first K steps of iterations, and use each step of the iterative process as a layer in MCNN to create a K layer feed-forward convolutional neural network. The present invention refers to this model-based convolutional neural network as a model-driven convolutional neural network MCNN. The feed-forward MCNN can be considered as an encoder.

[0035] The input of the encoder is a given ideal circuit layout, and its output is the corresponding OPC mask pattern. Here, "...

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Abstract

The invention discloses a deep learning method for computational lithography, which can improve the optimization speed and convergence performance of a traditional OPC (Optical Proximity Correction) method and can be used for deep ultraviolet DUV lithography and extreme ultraviolet EUV (Extreme Ultraviolet) lithography. The method comprises the steps of: developing a gradient iterative algorithm,intercepting a plurality of steps, combining a photoetching system imaging model and a photoresist model to create a forward MCNN (Model-driven Convolution Neural Network), namely an encoder; creatinga decoder corresponding to the encoder, connecting the output of the MCNN with the input of the decoder, taking a certain distance between the input of the MCNN and the output of the decoder as a cost function to train MCNN parameters; inputting training set samples into the MCNN, and optimizing the parameters in the MCNN to minimize the errors between the input of the MCNN and the output of thedecoder; and after the training is completed, separating the MCNN from the decoder, and adopting the MCNN to obtain OPC mask patterns corresponding to other circuit layouts.

Description

technical field [0001] The present invention relates to the technical fields of computational imaging, computational lithography and deep learning, in particular to a computational lithography method based on a Model-driven Convolution Neural Network (MCNN for short). Background technique [0002] Photolithography is one of the core technologies used to manufacture VLSI. The modern integrated circuit manufacturing industry continues to develop basically in accordance with Moore's Law. The chip's critical dimension (CD) is constantly shrinking. The development process from DUV (referred to as DUV) lithography to extreme ultraviolet (Extreme Ultraviolet, EUV) lithography. In different lithography technology nodes, different lithography systems may be required for integrated circuit fabrication. [0003] Projection lithography is widely used in the current VLSI mass production process due to its high resolution, no contamination of the mask, and good repeatability. In the DU...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G03F7/20G06N3/04
CPCG03F7/705G03F7/70508G06N3/045
Inventor 马旭郑现强
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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