Lithography Device Contribution Determination via Matrix Analysis
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Solution Overview
Problem
In lithographic processes, it is challenging to determine the individual contribution of each device to the fingerprint of a parameter associated with substrate processing, as multiple devices of the same type can affect substrates differently, leading to inconsistencies and difficulties in process control.
Innovation Solution
A method involving the use of parameter data and usage data to determine the contribution of each device to the substrate's fingerprint, utilizing a matrix-based approach to analyze the impact of various devices on substrate measurements, allowing for the identification of specific device effects on parameters like critical dimension and overlay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple devices are used to process substrates, then productivity is improved, but device complexity increases and measurement precision deteriorates due to difficulty in determining individual device contributions
Solution Approach 1:
The patent segments the overall processing effect into individual device contributions by creating separate measurement models for each device. The system divides the fingerprint analysis into device-specific components, allowing independent characterization of each device's impact on substrate parameters while maintaining the ability to process substrates through multiple devices in parallel or sequence.
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between raw measurement data and device contribution analysis. This model includes device-specific parameters and measurement matrices that translate complex multi-device processing effects into separable, quantifiable device contributions, enabling precise attribution without requiring physical separation of devices.
2Adaptability or versatility
If multiple devices process substrates with different effects, then adaptability is improved, but manufacturing precision deteriorates due to inconsistencies in parameter fingerprints
Solution Approach 1:
The patent applies local quality by assigning unique characteristic parameters to each device, allowing each device to have its own fingerprint profile. This enables the system to account for local variations in device performance while maintaining overall process consistency. Each device's specific effects are captured and compensated for individually, ensuring precision regardless of which device combination is used.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting device-specific parameters based on measured fingerprints. The system modifies processing parameters for individual devices based on their characteristic deviations, compensating for variations in device performance. This allows the system to adapt to different device states and maintain manufacturing precision across diverse device combinations.
3Device complexity
If individual device contributions are not determined, then device complexity is reduced, but loss of information increases regarding specific device effects on substrates
Solution Approach 1:
The patent creates computational copies of device characteristics through measurement matrices and parameter models. Instead of physically complex tracking systems, the system uses mathematical representations that replicate device-specific effects. These computational models capture and preserve device effect information in an easily manageable format, maintaining information integrity without adding physical complexity.
Data Source
AI summary
A method, system and program for determining a fingerprint of a parameter. The method includes determining a contribution from a device out of a plurality of devices to a fingerprint of a parameter. The method includes obtaining parameter data and usage data, wherein the parameter data is based on measurements for multiple substrates having been processed by the plurality of devices, and the usage data indicates which of the devices out of the plurality of the devices were used in the processing of each substrate; and determining the contribution using the usage data and parameter data.