Mask Pattern Resource Allocation for OPC Processing
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Solution Overview
Problem
The processing of mask data for semiconductor manufacturing requires significant computer resources, particularly for optical proximity correction (OPC) tasks, which are computationally intensive and time-consuming, necessitating a method to optimize resource allocation for efficient processing.
Innovation Solution
A computer-implemented method that estimates the expected time needed to process mask patterns and allocates them to processing cores accordingly, allowing for efficient distribution and reallocation of resources during multiple trials with different simulation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed processing is used to process mask patterns, then processing speed is improved, but resource allocation complexity increases
Solution Approach 1:
The system performs preliminary estimation of processing time for each mask pattern before actual processing begins. This advance assessment allows the resource allocation system to pre-determine optimal distribution of processing tasks across available cores, eliminating the need for complex dynamic reallocation during processing and reducing allocation complexity while maintaining high processing speed
2Manufacturing precision
If multiple trials with different simulation parameters are performed, then manufacturing precision is improved, but computational resource consumption increases
Solution Approach 1:
The system estimates processing requirements for each mask pattern before executing multiple trials with different simulation parameters. This preliminary assessment enables intelligent resource allocation that prepares the system to handle computationally intensive OPC tasks efficiently, reducing overall computational resource consumption while maintaining the precision benefits of multiple trials
Solution Approach 2:
The resource allocation system dynamically adjusts the distribution of mask patterns across processing cores based on estimated processing times and available resources. This dynamic allocation allows the system to optimize resource utilization for multiple trials, ensuring that computational resources are consumed efficiently while maintaining the manufacturing precision required for optical proximity correction
3Productivity
If more processing cores are allocated to mask patterns, then processing speed is improved, but resource management complexity increases
Solution Approach 1:
The system performs preliminary estimation of processing time for each mask pattern before allocation. This advance information simplifies resource management by providing clear guidance on how to distribute tasks across processing cores, making it easier to manage resources while maximizing processing speed through optimal core allocation
Data Source
AI summary
A computer-implemented method of managing resources for multiple trial distributed processing tasks is presented. The method includes estimating an expected time needed to process each of a set of mask patterns which can be independently processed. The method further includes allocating each of the set of mask patterns to a set of processing cores in accordance with the expected time, and processing the mask patterns in accordance with the allocation, when the computer in invoked to estimate, allocate, and process.