Solder Printing Parameter Optimization With Real-Time Yield Feedback
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Solution Overview
Problem
Solder printing apparatuses face challenges in optimizing control parameters in real-time due to variations in solder, stencil, and squeegee conditions, leading to suboptimal yields and inefficient substrate usage.
Innovation Solution
An electronic device connected to the solder printing apparatus and measurement systems generates and updates a model to search for optimal control parameter sets based on real-time data, ensuring yields meet or exceed a preset threshold without relying on historical data, thereby minimizing yield decreases and substrate wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time data collection and model updating is implemented, then manufacturing precision and yield are improved, but device complexity increases
Solution Approach 1:
The system implements real-time feedback by collecting solder measurement information from the measurement apparatus, comparing it against the surrogate model predictions, and using the actual yield data to update and refine the control parameter optimization. This closed-loop feedback mechanism enables continuous improvement of manufacturing precision through adaptive learning from actual process outcomes.
Solution Approach 2:
The surrogate model performs self-updating by automatically incorporating new measurement data and yield information to refine its predictions of optimal control parameters. This self-service capability allows the system to autonomously improve its optimization accuracy without requiring external intervention, thereby enhancing manufacturing precision while managing system complexity through automated processes.
2Adaptability or versatility
If historical data is used for model creation, then device complexity is reduced, but adaptability to current conditions deteriorates
Solution Approach 1:
The system transitions from static historical data models to dynamic real-time optimization by continuously updating the surrogate model with current measurement data. This dynamic approach allows the control parameter optimization to adapt to changing solder, stencil, and squeegee conditions, significantly improving adaptability to current printing conditions while managing complexity through efficient data utilization.
Solution Approach 2:
The system changes the temporal parameter of data usage from historical to real-time, incorporating freshly collected measurement information into the surrogate model updates. This parameter change enables the system to respond to current process conditions rather than relying on outdated historical patterns, thereby enhancing adaptability while maintaining manageable system complexity through focused data processing.
3Productivity
If optimal control parameters are not optimized in real-time, then substrate consumption is reduced, but yield decreases
Solution Approach 1:
The system performs preliminary optimization by predicting optimal control parameters before actual printing operations using the surrogate model. This preliminary action allows adjustments to be made in advance based on current conditions, preventing yield losses and reducing substrate wastage before they occur, thereby improving productivity while minimizing material loss.
Solution Approach 2:
The system rapidly iterates through control parameter optimizations by efficiently updating the surrogate model with new measurement data and quickly generating updated optimal parameter sets. This rushing through the optimization process enables real-time adaptation without significant delays, maintaining high productivity while preventing yield losses and substrate wastage through timely parameter adjustments.
Data Source
AI summary
An electronic apparatus according to various embodiments of the present disclosure may include: a communication circuit that is communicatively connected to a solder printing apparatus and a measurement apparatus; one or more memories; and one or more processors. One or more processors may be configured to: acquire a first control parameter set of the solder printing apparatus for printing solder on a first substrate; transmit information indicating the first control parameter set to the solder printing apparatus; acquire first solder measurement information indicating a state of the solder printed on the first substrate; determine a first yield for the first substrate based on the first solder measurement information; and generate a model for searching for an optimal control parameter set based on a first data pair including the first control parameter set and the first yield.