Electrostatic Droplet Ejection Using ML for Substrate Pattern Compensation
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Solution Overview
Problem
Existing electrostatic liquid droplet ejection methods are affected by variations in substrate patterns, leading to inconsistent droplet sizes and non-uniform pattern formation.
Innovation Solution
A machine learning-based approach that uses imaging data to generate liquid droplet ejection conditions, considering substrate patterns, conductivity, contact angle, and droplet shape, to stabilize electrostatic ejection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If electrostatic liquid droplet ejection is used, then finer liquid droplets can be ejected, but the ejection is affected by substrate patterns leading to inconsistent droplet sizes
Solution Approach 1:
The system performs preliminary imaging of the substrate pattern before ejection, and uses machine learning to predict and compensate for pattern-induced ejection variations in advance, ensuring consistent droplet sizes despite substrate variations
Solution Approach 2:
The system implements feedback by capturing images of the substrate pattern, feeding this information into a machine learning model to determine corrected ejection conditions, and using these corrected conditions to achieve uniform droplet ejection despite substrate variations
2Manufacturing precision
If electrostatic ejection is used, then finer liquid droplets can be ejected, but non-uniform pattern formation occurs due to substrate variations
Solution Approach 1:
The system performs preliminary imaging and analysis of the substrate pattern before ejection begins, using machine learning to predict how substrate variations will affect ejection, and pre-calculates corrected ejection conditions to ensure uniform pattern formation
Solution Approach 2:
The system changes ejection parameters (voltage, frequency, distance) based on substrate pattern analysis and machine learning predictions, dynamically adjusting these parameters to compensate for substrate variations and achieve uniform droplet placement
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
Stable and uniform liquid droplet ejection is achieved, unaffected by substrate patterns, enabling precise pattern formation.
Implementation Method 1
an electrostatic liquid droplet ejection nozzle that has a nozzle tip exposed on a first surface of the substrate and liquid supply, and that ejects liquid droplets onto the substrate in a direction toward the substrate from the nozzle tip
Data Source
AI summary
A liquid droplet ejection method is provided including capturing an image of a pattern on a substrate, acquiring first image data corresponding to the pattern, and applying the first image data to a machine learning model to perform machine learning and generate liquid droplet ejection conditions based on an electrostatic method. The liquid droplet ejection method described above may further include generating the machine learning model by performing machine learning on pre-acquired first image data.