Statistical Optical Proximity Correction for Mask Bias

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Solution Overview

Problem

Traditional optical proximity correction (OPC) techniques become less effective as technology advances to smaller ground rules, leading to manufacturing yield issues due to optical proximity effects and mask pattern fidelity limitations in photolithographic processes.

Innovation Solution

An OPC system that incorporates inline process variation data to adjust mask geometry, using a statistical OPC model and simulator to apply a mask bias, thereby correcting errors in the input mask pattern and optimizing critical dimension (CD) yield.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If traditional OPC techniques are used, then mask pattern fidelity can be maintained, but manufacturing precision deteriorates at smaller ground rules

Engineering Contradiction:

Improvefeature generation accuracyVSAvoidmanufacturing yield

Core Design Contradiction:

Manufacturing precisionVSReliability

Solution Approach 1:

The patent changes the parameters of the OPC model by incorporating inline process variation data (focus, exposure, PEB temperature variations) to create a statistical OPC model that accounts for manufacturing variations, thereby improving feature generation accuracy at smaller ground rules

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent transforms the static traditional OPC model into a dynamic statistical OPC model that adapts to process variations by using measured inline data to adjust correction parameters, enabling the system to respond to actual manufacturing conditions

Inventive Principle:

Principle #15Dynamics

2Manufacturing precision

If OPC models do not incorporate process variation data, then device complexity is reduced, but manufacturing precision deteriorates

Engineering Contradiction:

ImproveCD yieldVSAvoidOPC model complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary measurements of inline process variations (focus, exposure, PEB temperature) and incorporates this data into the OPC model in advance, allowing the statistical model to predict and correct for variations before actual manufacturing occurs

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a feedback mechanism where measured process variation data from the manufacturing line is fed back into the OPC model to continuously refine and adjust correction parameters, improving CD yield through iterative optimization

Inventive Principle:

Principle #23Feedback

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

The solution improves OPC accuracy by iteratively fine-tuning the mask bias, resulting in higher CD yield and reduced errors, effectively addressing the limitations of traditional OPC models.

Implementation Method 1

The photoresist layer is exposed by passing radiation from an exposure source through a mask

Methodology Applied

Scientific EffectAbsorption (EM radiation): Absorption (EM radiation)

Data Source

PatentUS8572524B2Statistical optical proximity correction

Publication Date: 2013.10.29 CHARTERED SEMICON MFG LTD

AI summary

An optical proximity correction (OPC) model incorporates inline process variation data. OPC is performed by adjusting an input mask pattern with a mask bias derived from the OPC model to correct errors in the input mask pattern.