Semiconductor Back-End Scheduling via Bottleneck Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current planning and scheduling systems in semiconductor manufacturing facilities, particularly in back-end factories, are inadequate to handle increasing complexity and stringent supply chain requirements, leading to inefficiencies and inability to meet demand deadlines.
Innovation Solution
A scheduling system comprising a planning module, scheduling module, and dispatching system connected by a block-based workflow engine, which generates a bottleneck loading plan, simulates factory operations, and creates a lot-to-machine schedule to optimize equipment utilization and meet demand deadlines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple Excel spreadsheets and manual schedules are used for planning, then ease of operation is maintained, but productivity and ability to meet complex supply chain requirements deteriorate
Solution Approach 1:
The patent replaces manual Excel-based planning systems with an automated simulation and optimization system that uses computational algorithms to generate production schedules. This substitution transforms the mechanical manual process into an automated electronic system that can handle complex constraints and optimize productivity without sacrificing ease of operation.
Solution Approach 2:
The system changes the parameters of the planning process by introducing simulation-based optimization with multiple objective functions (throughput maximization, due date meeting, work in process minimization). This allows the system to automatically adjust scheduling parameters to balance ease of operation with improved productivity and supply chain compliance.
2Device complexity
If manual planning methods are used, then device complexity is reduced, but manufacturing precision and ability to meet due dates worsen
Solution Approach 1:
The patent replaces simple manual planning tools with a sophisticated simulation and optimization system that automatically handles complex manufacturing constraints. This substitution increases system complexity but enables precise scheduling that meets due dates and optimizes manufacturing outcomes through automated decision-making.
Solution Approach 2:
The system creates a virtual copy of the manufacturing process through simulation, allowing planners to test and optimize schedules in a virtual environment before implementation. This copying approach enables precise planning without requiring complex physical changes to the manufacturing system.
3Productivity
If automated simulation and optimization systems are implemented, then productivity and ability to meet due dates improve, but device complexity increases
Solution Approach 1:
The patent segments the planning system into distinct functional modules: data input module, simulation engine, optimization module, and schedule generation module. This segmentation allows the complex system to be managed through separate, well-defined components that can be independently configured and maintained, reducing the effective complexity burden.
Solution Approach 2:
The simulation and optimization system is designed as a universal platform that can handle multiple objective functions (throughput maximization, due date meeting, work in process minimization) and various manufacturing scenarios. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated solution.
4Adaptability or versatility
If reentrant flows and complex processes are implemented to meet product variety demands, then adaptability improves, but manufacturing precision and schedule reliability worsen
Solution Approach 1:
The patent applies preliminary action by using simulation to pre-evaluate multiple scheduling scenarios and identify the most reliable schedule before actual production begins. This allows the system to account for reentrant flows and complex process interactions in advance, ensuring schedule reliability is optimized before implementation.
Solution Approach 2:
The system incorporates feedback mechanisms where simulation results feed back into the optimization process, allowing the schedule to be refined based on predicted performance. This feedback loop enables the system to automatically adjust schedules to maintain reliability even when dealing with complex reentrant flows and high adaptability requirements.
Data Source
AI summary
Embodiments presented herein provide techniques for planning and scheduling in a factory. The technique begins by generating a bottleneck loading plan from a plurality of inputs. A simulation is run using the bottleneck loading plan. The factory is simulated using decisions made based on the bottleneck loading plan and a lot-to-machine schedule is generated with the simulation bottleneck loading plan.