Semiconductor Scheduling via Dynamic Weight Adjustment
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Solution Overview
Problem
Current semiconductor fabrication scheduling methods are inefficient and labor-intensive, often relying on manual adjustments and algorithms that fail to optimize the dispatch of product lots to manufacturing equipment, leading to suboptimal processing times and equipment utilization.
Innovation Solution
A semiconductor fabrication scheduling method and system that uses a load-balancing model with weight factors, integrated with a big-data architecture to generate and adjust schedules based on key performance indicators, automatically optimizing the dispatch of product lots to workstations for improved efficiency and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling adjustment is used based on manufacturing personnel experience, then scheduling can be performed with simple system complexity, but the scheduling efficiency and productivity are low and the process is tedious
Solution Approach 1:
The scheduling system automatically adjusts weight factors based on historical KPI data and machine learning algorithms without requiring manual intervention. The system serves itself by autonomously optimizing scheduling parameters, eliminating the need for tedious manual adjustments while maintaining high scheduling efficiency.
Solution Approach 2:
The patent replaces manual mechanical scheduling adjustments with an automated computational system that uses big data architecture and machine learning models. This substitution transforms the scheduling process from a manual, experience-based mechanical operation to an automated, data-driven intelligent system.
2Productivity
If automated scheduling algorithms are used to assign lots to machines, then scheduling speed is improved, but the scheduling optimization effect is often undesired due to lack of adaptive adjustment
Solution Approach 1:
The system continuously monitors KPIs from completed fabrications and feeds this information back into the machine learning model. The model uses this feedback to automatically adjust weight factors, creating a closed-loop system that continuously improves scheduling optimization效果 based on actual performance data.
Solution Approach 2:
The scheduling system transitions from static, fixed algorithms to dynamic, adaptive algorithms that automatically adjust their parameters (weight factors) based on real-time KPI data and historical patterns. This dynamic adjustment enables the system to adapt to changing manufacturing conditions and maintain optimal scheduling effectiveness.
3Ease of manufacture
If load balancing is optimized using fixed weight factors in objective functions, then the scheduling model is simple to implement, but it cannot adapt to changing manufacturing conditions and KPIs
Solution Approach 1:
The system implements dynamic weight factors that automatically adjust based on KPI performance and historical data. This transforms the objective function from a static, fixed-parameter model to a dynamic, adaptive model that automatically responds to changing manufacturing conditions while maintaining implementation feasibility through automated adjustment.
Solution Approach 2:
The scheduling system automatically adjusts its own weight factors without requiring manual reconfiguration or complex implementation procedures. The self-adjusting mechanism maintains model simplicity while achieving adaptability, as the system autonomously optimizes its parameters based on performance feedback.
4Productivity
If manual scheduling adjustment is performed to avoid long waiting times and equipment starvation, then equipment utilization can be improved, but the process is tedious and time-consuming
Solution Approach 1:
The system autonomously monitors equipment status and automatically adjusts scheduling to prevent starvation and optimize utilization. This self-service capability eliminates the need for manual intervention while maintaining high equipment utilization, thereby saving time without sacrificing performance.
Solution Approach 2:
The scheduling system operates continuously and automatically, constantly monitoring and adjusting to maintain optimal equipment utilization. This continuous automated operation eliminates the intermittent, tedious manual adjustments while sustaining high productivity and equipment usage throughout the manufacturing process.
Data Source
AI summary
A semiconductor fabrication scheduling method includes creating a load scheduling data schema including facility data of product lots to be dispatched to a plurality of workstations; generating a load schedule profile using a load-balancing model and based on the load scheduling data schema, wherein the load-balancing model includes one or more objective functions and there is at least one weight factor in an objective function; generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.