Abstract: The photolithography area is a critical component of the chip manufacturing process and is constrained by various factors such as low equipment utilization, extended waiting time for chip processing, and limitations related to photomasks. These constraints create bottlenecks in the production scheduling of the photolithography area, ultimately impacting chip yield and manufacturing costs. With the continuous advancement of chip technology and increasing market demand, there is a growing need for resources in the photolithography area. Researchers have turned their attention to optimizing the production scheduling problem in the photolithography area to enhance operational efficiency and capacity, thus improving chip yield and reducing manufacturing costs. Existing production scheduling models for the photolithography area are primarily based on single-objective optimization, which fails to comprehensively balance and consider the interrelationships among various factors in actual production. This study aims to propose an innovative chip production scheduling model for the photolithography area based on multi-objective optimization, aiming to significantly reduce equipment idling and minimize total chip processing waiting time. In this way, the lithography chip can be produced in the shortest time and with the highest efficiency.
Drawing from multi-objective scheduling optimization theory and specific enterprise examples, a multi-objective optimization model for the production scheduling of photolithography area chips has been developed. The model also includes the optimization of chip weights, enhancing its scientific foundation. To solve this model, the study has designed a multi-strategy improved multi-objective sparrow algorithm, incorporating the Halton sequence chaotic improved initial value, external archive update mechanism, non-dominant sorting, discoverer polynomial mutation, and population self-adaptive control. In the process of solving large datasets, the multi-strategy improved sparrow search algorithm demonstrates the best performance. The research integrates the actual production scenario in the photolithography area and simulates data based on real workshop data.
Through experimental analysis of the multi-strategy improved multi-objective sparrow search algorithm, it is observed that the algorithm exhibits the best performance in problem-solving. Even with changes in chip weights, the algorithm maintains good stability and reliability. The optimized scheduling model demonstrates clear advantages in multiple aspects. Compared with the existing scheduling model, the optimized model effectively addresses the allocation scheme of the photolithography area, significantly reducing production time. By accurately analyzing the matching relationship between the photolithography area and the machine stage, and considering factors such as the state and work efficiency of the machine stage, the photolithography area can be allocated to the machine stage more reasonably, thereby enhancing production efficiency.
The study holds significant importance in the field of chip production, providing a practical method to optimize chip production scheduling in the photolithography area. Future research can explore further multi-objective optimization algorithms and consider additional constraint conditions to further enhance the production scheduling effect in the photolithography area. This is of great significance for improving the production efficiency and competitiveness of the manufacturing industry.

Key words: lithography area, chip, production scheduling, multi-objective optimization, sparrow search algorithm

摘要： 光刻区是芯片制造车间中加工量最低的区域。主要原因是因为光刻区加工芯片需要等待时间长,设备利用率低,约束条件多,以及生产调度系统存在问题。因此,提升芯片产量的主要方法之一是优化光刻区现有的生产调度模式。本文针对光刻区调度问题,构建了基于芯片生产等待时间最小化、设备综合效率最大化和光罩约束条件下的光刻区芯片生产调度多目标优化模型。考虑到芯片的重要程度也会影响生产调度模型,研究同时对芯片权重进行了优化。结合问题特征,设计了多策略改进的多目标麻雀搜索算法来求解模型。研究发现,多策略改进的多目标麻雀算法在芯片权重变化时仍然具有良好的稳定性,并且优于现有调度模型。在大数据求解过程中,多策略改进的多目标麻雀算法表现最优,可以有效解决光刻区对应机台的分配问题,减少生产时间,提升机台效率,从而扩大了光刻区产能。

关键词: 光刻区, 芯片, 生产调度, 多目标优化, 麻雀搜索算法