Resist Pattern Prediction Using Volterra Kernel OPC
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Solution Overview
Problem
Current photolithography processes in semiconductor manufacturing face challenges in accurately predicting resist patterns, which affects the precision and quality of microelectronic circuits.
Innovation Solution
A resist pattern prediction device is developed, incorporating an optical proximity correction module and a pattern prediction module. The module performs optical and non-optical proximity corrections using a Volterra kernel based on a quadratic term of a Volterra series, enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional photolithography processes are used, then manufacturing simplicity is maintained, but resist pattern prediction accuracy deteriorates
Solution Approach 1:
The correction process is segmented into two distinct modules: optical proximity correction (OPC) that handles optical effects, and non-optical proximity correction that handles resist and process effects. This segmentation allows each module to specialize in specific physical phenomena, improving overall prediction accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent extends the correction approach from traditional 2D mask space to 3D resist space by incorporating depth information and volumetric effects in the non-optical proximity correction. This dimensional extension captures more physical realities of the lithography process, significantly improving prediction accuracy for actual resist patterns formed on wafers.
2Measurement precision
If simple correction methods are used, then processing speed is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The system performs preliminary optical proximity correction to generate an initial aerial image before applying non-optical proximity correction. This preliminary action establishes a foundation that reduces the computational burden of subsequent corrections, allowing higher accuracy methods to run faster by starting from a pre-corrected state rather than raw mask data.
Solution Approach 2:
The aerial image serves as an intermediary representation between the mask pattern and the final resist pattern prediction. This intermediate form allows the system to apply different correction models at appropriate stages, with optical corrections on the aerial image and non-optical corrections refining the prediction, thereby balancing accuracy requirements with computational efficiency.
Data Source
AI summary
Disclosed is a resist pattern prediction device, which includes an optical proximity correction module for generating both an optical proximity correction and a non-optical proximity correction. The optical proximity correction module generates an aerial image by performing an optical proximity correction based on a mask image. The module also generates a resist image by performing a non-optical proximity correction on the mask image and the aerial image. The resist pattern prediction device also includes a pattern prediction module that predicts information with respect to a resist pattern based on the resist image. The non-optical proximity correction includes performing a convolution operation on the aerial image using a Volterra kernel based on a coefficient of a quadratic term of a Volterra series.