基于神经网络方法的半导体生产工艺优化

以提高生产成品率为目标，利用神经网络的非线性和容错性，对半导体芯片生产过程进行了分析和优化，具体内容如下：（１）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，构造以工艺参数为输入，成品率为输出的多维函数曲面．（２）对上述多维函数曲面进行搜索，搜索成品率最高的最优点，以该最优点的工艺参数值为依据确定工艺参数的规范值．（３）对工艺参数规范进行优化，在实际生产工艺中反复实践，直至达到提高成品率的目的．生产实践证明，神经网络的分析结果是合理的．根据神经网络分析提出的优化建议，有效地提高了工

Neural Network-Based Optimization of VLSI Wafer Fabrication

A neural|based manufacturing process control system for semiconductor factories is presented. Wafer fabrication is a dynamic, nonlinear,multivariable and complex industrial process.A model based on feedforward neural networks(FNN) 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. A gradientdescent method to search a set of optimal technological parameters is used in order to lead to the maximum yield by simulation. The wafer yield increases by 7^63% after the optimal parameters were applied in the wafer fabrication assembly.