Nanoimprint Lithography Alignment Control via Hybrid Feedforward Feedback
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Solution Overview
Problem
Current alignment schemes in nanoimprint lithography face challenges such as slow alignment convergence, overshoot, undershoot, stalling, and oscillation due to thin-liquid friction and residual layer thickness variations, leading to inefficiencies in mass production.
Innovation Solution
A method involving real-time feedforward and feedback control using multiple feedforward signals integrated with feedback signals to continually update and refine the control signals for the moveable stage, allowing for rapid and accurate alignment correction by monitoring position information and adjusting parameter values such as acceleration, velocity, and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single control algorithm with manual tuning is used for alignment, then the control system is simple to implement, but alignment convergence is slow and exhibits oscillation and stalling
Solution Approach 1:
The control algorithm is segmented into multiple independent components: a feedforward controller that generates reference trajectories, a feedback controller that corrects errors, and a hybrid controller that combines both. This segmentation allows each component to be optimized independently, resolving the contradiction between simplicity and performance by composing simple modular elements into a complex high-performance system.
Solution Approach 2:
The control system transitions from static manual tuning to dynamic adaptive control. The feedforward controller uses pre-calculated trajectories that adapt to different alignment scenarios, while the feedback controller dynamically adjusts based on real-time error signals. This dynamic approach enables fast convergence without oscillation, overcoming the limitations of fixed manual tuning parameters.
2Ease of operation
If manual tuning of control parameters is performed, then the control algorithm is easy to configure, but time delay occurs when implementing modifications
Solution Approach 1:
The feedforward controller pre-calculates optimal reference trajectories before the alignment process begins, based on the initial alignment error and desired final position. This preliminary action eliminates the need for real-time parameter tuning during alignment, as the optimal control parameters are already determined in advance, thus removing time delays associated with manual adjustments.
3Stability of the object's composition
If conventional control schemes are used, then the system is stable, but it cannot handle variations in residual layer thickness, location, and transition process
Solution Approach 1:
The feedback controller continuously monitors the alignment error between template and substrate marks and generates correction signals based on the difference from the reference trajectory. This closed-loop feedback mechanism automatically adapts to variations in residual layer thickness and location, maintaining stability while handling diverse conditions, thus resolving the contradiction between stability and adaptability.
Solution Approach 2:
The control system dynamically adjusts control parameters based on the measured alignment error and system state. The feedforward controller modifies reference trajectories according to initial error conditions, while the feedback controller adjusts correction magnitudes based on real-time performance. These parameter changes enable the system to adapt to varying RLT and location conditions while maintaining stable operation.
4Productivity
If fast alignment is implemented, then productivity increases, but alignment accuracy and repeatability deteriorate
Solution Approach 1:
The reference trajectory generated by the feedforward controller serves as an intermediary between the initial misalignment state and the final aligned state. Instead of directly correcting large errors which causes overshoot and oscillation, the system follows a pre-planned intermediate path that smoothly transitions the alignment, maintaining both speed and accuracy. This intermediary trajectory enables fast alignment without sacrificing precision or repeatability.
Data Source
AI summary
A method and system for controlling a position of a moveable stage having a substrate supported thereon is provided. First position information representing a position of the substrate relative to a mark on an object is obtained from a sensor. Alignment prediction information is generated based on the obtained first position wherein the generated alignment prediction information including at least one parameter value. First trajectory information is generated and includes the at least one parameter value based on the obtained first position information and the generated alignment prediction information. Second trajectory information is generated based on the generated alignment prediction information first trajectory information and second position information, wherein the second position information represents a position of the moveable stage. An output control signal is generated based on the second trajectory information and used to control the moveable stage to approach a target position based on the generated output signal.