基于神经网络的集成电路生产过程建模与优化

以提高半导体生产线的成品率为目标,利用神经网络对半导体芯片生产过程进行了建模和优化.首先使用神经网络方法建立模型,确定生产线上工艺参数和成品率之间的映射关系,构造多维映射函数曲面;随后对多维映射函数曲面进行搜索,搜索成品率最高的最优点,据此确定工艺参数的规范值;最后,根据优化后的工艺参数规范进行实际生产.采用这种优化建议,半导体生产线的平均成品率由51.7%提高到了57.5%.

Neural Network-Based Optimization of Vlsi Wafer Fabrication

In this paper, we present a neural based manufacturing process control system to improve the lot yield of wafer fab. The process is as follows: 1. A model based on feedforward neural networks is proposed to simulate the wafer manufacturing process. Learning from the historical technological records with a special dynamic learning method, the neural based model can approximate the function relationship between the technological parameters and the wafer yield precisely. 2. We also use a gradient descent method to search a set of optimal technological parameters that lead to the maximum yield by simulation. 3. We optimize the specifications of the wafer fab according to the optimal parameters. The wafer yield increases by 11 2% after the optimized specifications are applied to the wafer fabrication assembly.