Semiconductor Feature Defect Probability Analysis
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Solution Overview
Problem
Current semiconductor manufacturing processes face challenges in accurately reproducing patterns on substrates with features smaller than the classical resolution limit of lithographic projection apparatuses, leading to defects such as missing or merged features due to stochastic variations in line edge and width roughness, which affect the production yield and quality of integrated circuits.
Innovation Solution
A method is developed to determine the probability of defective manufacture by analyzing image data from substrates, using statistical parameters like mean and standard deviation of feature dimensions, and optimizing processing conditions to minimize stochastic variations through a system that combines image analysis with lithographic and metrology tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lithographic projection apparatuses are used to manufacture features smaller than the classical resolution limit, then the productivity and functional element density increase, but the manufacturing precision deteriorates due to stochastic variations causing missing or merged features
Solution Approach 1:
The patent applies optical proximity correction (OPC) and resolution enhancement techniques before the actual lithography process to pre-compensate for expected stochastic variations. This preliminary action modifies the patterning device design and illumination parameters to anticipate and counteract the merging or missing features that would otherwise occur during manufacturing, thereby maintaining manufacturing precision while enabling higher functional element density
Solution Approach 2:
The patent systematically varies multiple process parameters including illumination wavelength, numerical aperture, resist composition, and exposure dose to find optimal combinations that extend the resolution limit. By changing these parameters, the system achieves better control over stochastic variations, allowing manufacturing of smaller features with acceptable precision and higher productivity
2Reliability
If conventional lithographic processes are used without statistical analysis, then the process complexity remains low, but the reliability of pattern reproduction deteriorates due to uncontrolled stochastic variations
Solution Approach 1:
The patent implements a feedback mechanism where measured feature dimensions from test structures are fed back to adjust and optimize the lithography process parameters. This closed-loop approach uses statistical analysis of measured data to identify stochastic variation patterns and continuously refines the manufacturing process, significantly improving pattern reproduction accuracy while the automated nature of the feedback system manages the increased process complexity
Solution Approach 2:
The patent replaces traditional trial-and-error mechanical process adjustment with automated statistical analysis and computational optimization. By using software-based statistical models and algorithms to analyze measurement data and predict optimal process parameters, the system achieves higher reliability in pattern reproduction while the automation actually reduces the operational complexity despite the sophisticated analysis involved
Data Source
AI summary
A method of determining a characteristic of one or more processes for manufacturing features on a substrate, the method including: obtaining image data of a plurality of features on a least part of at least one region on a substrate; using the image data to obtain measured data of one or more dimensions of each of at least some of the plurality of features; determining a statistical parameter that is dependent on the variation of the measured data of one or more dimensions of each of at least some of the plurality of features; determining a probability of defective manufacture of features in dependence on a determined number of defective features in the image data; and determining the characteristic of the one or more processes to have the probability of defective manufacture of features and the statistical parameter.