Deep-Learning OPC Modeling for Accurate Semiconductor Pattern Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optical proximity correction (OPC) methods in semiconductor manufacturing are limited in their ability to accurately predict and correct for optical proximity effects and photoresist characteristics during the exposure process, leading to inaccuracies in pattern transfer on semiconductor wafers.
Innovation Solution
An OPC method utilizing deep learning techniques, including a first OPC model reflecting optical phenomena and a second OPC model reflecting photoresist characteristics, uses a convolutional neural network (CNN) with shared parameters and down-sampling methods like sinc filters and erosion to generate an optical proximity corrected design layout, followed by mask manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional OPC methods are used, then the manufacturing process is simple, but the accuracy of pattern transfer is insufficient
Solution Approach 1:
The OPC model is segmented into two distinct components: a first OPC model reflecting optical phenomena and a second OPC model reflecting photoresist characteristics. This segmentation allows each model to specialize in specific physical effects, improving overall prediction accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent combines multiple modeling approaches into a composite OPC model that integrates optical modeling and photoresist modeling. This composite structure leverages the strengths of different modeling techniques to achieve superior pattern transfer accuracy compared to conventional single-model approaches.
2Productivity
If down-sampling is performed without proper filtering, then computational efficiency improves, but grid dependency and aliasing increase
Solution Approach 1:
The patent applies preliminary down-sampling operations before the main OPC computation to reduce the complexity of the input data. By performing this preprocessing step early in the workflow, the system achieves better computational efficiency while the subsequent modeling steps compensate for any potential loss in precision.
Solution Approach 2:
The patent employs different down-sampling parameters and methods (including various filter types and erosion operations) to optimize the balance between computational efficiency and pattern accuracy. By adjusting these parameters, the system can adapt to different manufacturing requirements and pattern complexities.
Data Source
AI summary
An optical proximity correction (OPC) method using a deep learning-based OPC model having improved performance and a mask manufacturing method including the OPC method are provided. The OPC method includes receiving a design layout of a target pattern, generating a first OPC model reflecting an optical phenomenon in an exposure process, with respect to the design layout, generating a second OPC model reflecting a physical characteristic of a photoresist in the exposure process, and obtaining an optical proximity corrected (OPCed) design layout by performing a simulation using an OPC model including the first OPC model and the second OPC model. Generating the second OPC model uses a first result value obtained by down-sampling an input value by using a sinc filter and a second result value obtained by down-sampling the input value by using erosion.