Adaptive Algorithm for Lithographic OPC Recipe Generation

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Solution Overview

Problem

Current semiconductor manufacturing processes face challenges in optimizing optical proximity correction (OPC) recipes due to the complexity of handling trillions of parameters and the need for expert knowledge, leading to time-consuming and laborious manual processes for generating OPC recipes and associated lithographic masks, especially with the increasing complexity and miniaturization of semiconductor features.

Innovation Solution

An adaptive AI-driven multi-objective algorithm that analyzes prior outcome data to generate optimal OPC recipes and mask configurations, automatically partitioning the search space and optimizing trillions of parameters while meeting constraints, without requiring imaging data, by using predictive methods to determine efficient parameter settings for lithography processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual OPC recipe optimization methods are used, then expert knowledge and control are maintained, but the process becomes time-consuming and laborious

Engineering Contradiction:

ImproveOPC recipe optimization accuracyVSAvoidtime required for parameter tuning

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically optimizing OPC recipes through iterative simulation and evaluation without requiring manual expert intervention. The automated system evaluates multiple candidate recipes, predicts lithographic outcomes, and selects optimal parameters independently, eliminating the time-consuming manual tuning process while maintaining optimization quality

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

An automated optimization system acts as an intermediary between the lithographic process parameters and the desired outcomes. This intermediary system uses simulation models and evaluation metrics to bridge the gap between raw parameters and optimized recipes, replacing manual expert judgment with algorithmic decision-making that accelerates the process

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If comprehensive parameter optimization is performed, then OPC recipe quality improves, but the complexity of handling trillions of parameters increases

Engineering Contradiction:

Improvemask fidelityVSAvoidparameter optimization complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system segments the vast parameter space into manageable subsets by identifying and prioritizing critical parameters that have the most significant impact on lithographic outcomes. Rather than optimizing all trillions of parameters simultaneously, the method focuses computational resources on key parameters, reducing complexity while maintaining optimization effectiveness

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The optimization approach applies local quality by tailoring the optimization depth and parameter selection to specific regions of the mask and specific feature types. Different parameter sets and optimization strategies are applied locally to different areas based on their unique requirements, reducing overall complexity while improving local mask fidelity

Inventive Principle:

Principle #3Local quality

3Manufacturing precision

If manual analysis of petabytes of data is undertaken, then detailed OPC recipe generation is achieved, but the process becomes laborious and requires expert knowledge

Engineering Contradiction:

ImproveOPC recipe accuracyVSAvoidease of recipe generation

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system replaces the mechanical process of manual data analysis with automated computational methods. Instead of experts manually examining petabytes of lithographic data and adjusting parameters, the automated system uses simulation models and algorithms to process the data, evaluate outcomes, and generate optimized recipes, dramatically improving ease of operation while maintaining accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The method uses simulated copies and models of the lithographic process to predict outcomes without requiring extensive manual analysis of actual production data. By creating virtual representations of the manufacturing process, the system can evaluate and optimize recipes in silico, reducing the need for manual data examination while preserving optimization quality

Inventive Principle:

Principle #26Copying

Data Source

PatentUS10877367B2Adaptive algorithm to generate optical proximity correction lithographic recipe

Publication Date: 2020.12.29 INTEL CORP

AI summary

A machine readable storage medium, a method and an apparatus. The method comprises selecting a candidate set of parameters from a plurality of available parameters comprising variables that affect an outcome of a lithography process; performing a set of optimizations wherein each optimization of the set of optimizations is subject to a plurality of objectives and tolerances and a set of constraints, wherein performance of said each optimization comprises: modifying values of at least a portion of the candidate set of parameters to derive a predicted outcome for said each optimization; and determining whether a difference between the predicted outcome and an intended outcome is within an error threshold; and if the difference exceeds the error threshold, perform a subsequent optimization, and otherwise generate an input file including modified values, corresponding to a last one of the set of optimizations, for the at least a portion of the candidate set of parameters.