Optical Proximity Correction Model Using Asymmetric Test Structures
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Solution Overview
Problem
Current photolithographic processes face challenges in accurately sizing features due to optical distortions and variations caused by diffraction and scattering, particularly in submicron feature sizes, where standardized symmetrical structures fail to correct for asymmetric structures and post-processing effects like etch and clean processes, leading to performance degradation and higher failure rates.
Innovation Solution
A method and system that optimize feature sizing by using both symmetric and asymmetric test structures to generate OPC data, accounting for density-based responses from imaging and substrate material processing, which involves measuring critical dimensions post-processing to create a more accurate OPC model that corrects for optical, resist, and etch processing effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standardized symmetrical structures are used for OPC modeling, then the manufacturing process is simplified and easier to implement, but the measurement precision and manufacturing precision deteriorate because they fail to account for asymmetric structures and post-processing effects
Solution Approach 1:
The patent applies asymmetry by introducing asymmetric test structures alongside symmetric ones in the OPC modeling process. The method measures critical dimensions of both symmetric and asymmetric features after photolithographic processing, then uses these measurements to generate OPC data that accounts for asymmetric loading effects. This resolves the contradiction by maintaining the simplicity of standardized testing while improving precision through the inclusion of asymmetric structure measurements that capture post-processing effects like etch and clean processes.
Solution Approach 2:
The patent changes the parameters being measured by including both symmetric and asymmetric critical dimension data in the OPC model generation. Instead of relying solely on traditional symmetric structure measurements, the method incorporates asymmetric feature measurements that reflect actual post-processing variations. This parameter expansion allows the OPC model to compensate for density-based responses in material processing, thereby improving feature sizing accuracy without significantly complicating the manufacturing process.
2Productivity
If feature sizes are continuously decreased to increase packing density, then the productivity and device capacity increase, but the manufacturing precision deteriorates due to increased optical distortions and diffraction effects
Solution Approach 1:
The patent implements feedback by measuring the actual critical dimensions of both symmetric and asymmetric features after photolithographic processing, then using these measurements to generate corrected OPC data. This closed-loop approach allows the system to detect and compensate for optical distortions and diffraction effects that become more pronounced at smaller feature sizes. The feedback mechanism enables continuous optimization of feature sizing accuracy as packing density increases, resolving the contradiction between productivity and manufacturing precision.
3Device complexity
If only symmetric test structures are used for OPC data generation, then the device complexity is reduced, but the measurement precision deteriorates because asymmetric structures and their density-based responses are not accounted for
Solution Approach 1:
The patent applies segmentation by dividing the test structures into distinct symmetric and asymmetric categories, each measured and analyzed separately. The method processes symmetric features to capture baseline optical effects and asymmetric features to capture density-based responses from post-processing. This segmented approach allows the system to maintain relatively simple test structures while improving measurement precision through the targeted inclusion of asymmetric feature measurements that reveal processing effects missed by symmetric structures alone.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly minimizes variations in feature sizing and improves device performance by accounting for both symmetric and asymmetric structures, reducing static Iddq and yield roll-off with decreasing pitch, thereby enhancing the accuracy and reliability of semiconductor device fabrication.
Implementation Method 1
light passing through the reticle is refracted and scattered by the edges of the chrome mask patterns of the reticle
Implementation Method 2
light passing through the reticle is refracted and scattered by the edges of the chrome mask patterns of the reticle
Implementation Method 3
Positive resist, when exposed to radiation becomes more soluble and is thus more easily removed in a development process. Negative resist, in contrast, becomes less soluble when exposed to radiation.
Data Source
AI summary
A method (300) for generating an optical proximity correction model for a mask layout having an asymmetric feature structure includes fabricating a mask (310) having a plurality of symmetric and asymmetric test structures thereon, and image processing one or more semiconductor wafers (320) using the fabricated mask to create a plurality of symmetric and asymmetric resist structures overlying the one or more wafers. At least one critical dimension of the symmetric resist structures and the asymmetric resist structures are measured (330), thereby generating symmetric and asymmetric critical dimension data, and a difference between a desired feature size of the symmetric and asymmetric structures and the measured feature size of the symmetric and asymmetric structures is evaluated (380) in order to generate an optical proximity correction model (398) based thereon.