Substrate Processing Task Scheduling Using Dependency Graphs
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Solution Overview
Problem
Conventional substrate processing systems experience significant delays and inefficiencies due to interruptions in tasks, as they follow a fixed order and halt all subsequent tasks when one task is delayed or fails, leading to prolonged start-up times and reduced productivity.
Innovation Solution
A dynamic scheduling method based on task dependencies is implemented, where a processing device determines and generates a dependency graph, topologically sorts it to create a schedule, and adjusts the schedule dynamically if a task becomes unavailable, allowing other tasks that do not depend on the interrupted task to proceed, thereby minimizing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed order scheduling method is used, then task execution simplicity is maintained, but productivity decreases due to prolonged processing time when delays occur
Solution Approach 1:
The patent implements dynamic scheduling by constructing a dependency graph that represents task relationships and using topological sorting to generate executable schedules. When delays or failures occur, the system dynamically recalculates the schedule based on updated task states, allowing the scheduling approach to adapt to changing conditions rather than following a rigid fixed order.
Solution Approach 2:
The patent segments the overall task execution into independent tasks with defined dependencies. By representing tasks as nodes in a dependency graph with directed edges indicating precedence relationships, the system can identify and execute independent tasks in parallel or reorder them dynamically without affecting the entire schedule, thus improving productivity when delays occur.
2Reliability
If all subsequent tasks are halted when one task is delayed, then task dependency constraints are maintained, but loss of time increases due to cascading delays
Solution Approach 1:
The system dynamically adjusts task execution based on real-time dependency analysis. When a task is delayed or fails, the dependency graph is updated and re-topologically sorted to identify which subsequent tasks can still be executed. This allows the system to maintain dependency constraints for affected tasks while allowing unrelated tasks to proceed, minimizing cascading delays.
Solution Approach 2:
The patent extracts and isolates the impact of delays to specific dependent task chains. By using the dependency graph structure, the system identifies only those tasks that are directly or indirectly affected by a delay, separating them from independent tasks that can continue uninterrupted. This prevents unnecessary halting of unrelated tasks and reduces overall time loss.
3Productivity
If a dynamic scheduling system with dependency graphs is implemented, then productivity improves by allowing parallel execution, but device complexity increases due to graph construction and topological sorting
Solution Approach 1:
The patent replaces manual or rule-based scheduling mechanisms with an automated computational system that uses graph theory algorithms. The dependency graph construction and topological sorting are performed automatically by a processing system, eliminating the need for complex manual scheduling logic while enabling efficient parallel task identification and execution.
Solution Approach 2:
The scheduling system is self-adjusting through automated dependency analysis. When task states change, the system automatically updates the dependency graph and recalculates the execution order without external intervention. This automation handles the complexity internally while presenting a simple interface for task management, improving productivity without requiring users to manage the underlying complexity.
Data Source
AI summary
A method includes determining a plurality of dependencies associated with a plurality of tasks of a substrate processing system. The method further includes generating, based on the plurality of dependencies, a dependency graph of the plurality of tasks. The method further includes topologically sorting the dependency graph to generate one or more outputs. A schedule associated with processing a plurality of substrates in the substrate processing system is based on the one or more outputs.