晶圆制造工期预测是晶圆制造系统运行优化的核心问题,是保障晶圆产品准时交付的关键.深度学习方法从海量数据中识别规律,在静态环境下的产品工期预测问题中取得了良好的效果.然而,在车间在制品水平等因素动态变化下,当前方法通过构建复杂系统的黑箱模型预测晶圆制造工期,缺乏可解释性,难以阐述模型随系统状态的变动规律.提出一种面向晶圆制造工期预测的可解释深度学习方法(Interpretable deep learning method,IDLM),具体包括构建脑启发的深度神经网络结构解析模型,从"神经元→神经环路→神经网络"三个层面为预测网络解析提供结构基础;设计工期预测网络的关键神经元识别算法,利用信息熵权规则滤取工期预测网络中的关键神经元;提出工期预测网络关键神经环路搜索算法,快速搜索相似神经元优化组合以得到关键预测环路.试验结果表明,IDLM可在保持工期预测精度的同时提取出预测神经网络的关键神经环路,为动态环境下工期预测网络的自适应优化提供基础.
Interpretable Deep Learning Method for Wafer Manufacturing Cycle Time Forecasting
Wafer manufacturing cycle time forecasting is the core problem of semiconductor wafer fabrication system operation optimization,which is the key to guaranteeing the on-time delivery of wafer products.Deep learning methods learn the data fluctuation laws from massive data,construct black-box prediction models of complex systems,and achieve impressive prediction accuracy in static environments.However,under dynamic system state fluctuation,such as workshop work-in-process levels,current methods cannot stay accurate prediction due to the lack of interpretability to explain the changing rules of the forecasting model with system states.Therefore,an interpretable deep learning method(IDLM)for wafer manufacturing cycle time forecasting is proposed to clarify the organization rules of forecasting neural networks under different system states.First,a brain-inspired interpretable structural model of the wafer manufacturing cycle time forecasting neural network is constructed to provide a structural basis for the analysis of the network in the organization form of"neurons-neural circuits-neural network".Second,a key neuron recognition method of cycle time forecasting network is proposed to filter important neurons from the network with information entropy weighted rules constraint.Finally,a key neural circuit search algorithm is designed to quickly search for the optimal combination of similar neurons to obtain the key forecasting circuits.The experimental results show that IPM can extract the key neural circuits of the forecasting network while maintaining the accuracy,which provides a key structural basis for the network self-assembly under dynamic environments.
deep neural networkinterpretable learningnetwork parsingcycle time forecastingsemiconductor manufacturing
万方数据
维普
