稳健估计用于氮化铝金属化工艺的研究  

Study on Metallization of Aluminum Nitride using Robust Estimation Method

作　　者：胡向洋[1] 汪荣昌[1] 邵丙铣[1] 宗祥福[1] 

机构地区：[1]复旦大学材料科学系,上海200433

出　　处：《材料科学与工程》2000年第2期78-81,共4页Materials Science and Engineering

基　　金：国家自然科学基金!重点项目资助 (6 9836 0 30 )

摘　　要：应用化学镀镍的方法实现了氮化铝的金属化。为得到较大的氮化铝金属化层粘附力 ,运用基于稳健估计的神经网络研究氮化铝金属化中化学镀镍的反应参数与金属层粘附力的关系。为使神经网络更加稳健 ,本文根据统计学原理 ,在前馈神经网络基础上 ,采取稳健估计方法改进神经网络。建立了定量预测粘附力性能的模型 ,并进行实验验证。确定金属化工艺中稳定的优化工作区域。结果表明 ,稳健估计方法既有传统神经网络的优点 ,又有较强的抵抗异常值的能力 ,具有较广泛的实用性。Electroless plating method is applied to metallize the AlN sbstrate. The improved neural network is applied in study of the relationship between reaction parameters of electroless plating nickel in the process of the metallization of aluminum nitride and the adhesion strength between the metal layer and the substrate. To make neural network more robust, a novel robust estimation function is proposed to improve the feedforward neural network according to the theorem of statistics. The quantitative prediction model is developed. The optimum operation region of the process parameters is found in the new neural network. The results show merits of robust estimation, which not only holds the merit of traditional neural network, but also has robustness against outliers.

关 键 词：稳健估计 化学镀镍 氮化铝 集成电路 封装 

分 类 号：TN405.94[电子电信—微电子学与固体电子学] TQ153.12[化学工程—电化学工业]