根据脉冲激光沉积(PLD)法在单晶Si试样表面沉积制备多层TiN/AlN硬质膜实验数据,应用基于粒子群算法(PSO)寻优的支持向量回归(SVR)方法,建立不同工艺参数下沉积的TiN/AlN多层膜的A1N膜厚及TiN薄膜硬度的SVR预测模型.在相同的训练与测试样本集下,将SVR所得的AlN膜厚预测值与免疫径向基函数(IRBF)神经网络的计算结果进行比较.结果表明,SVR模型训练和预测结果的平均绝对百分误差要比IRBFNN模型的小,其预测精度更高,预测效果更好.应用SVR的TiN薄膜硬度模型对PLD法沉积TiN薄膜的工艺参数进行了优化,分析了多因素对PLD法沉积TiN薄膜硬度的交互作用和影响.该方法可为人们利用PLD法沉积TiN/AlN多层功能薄膜提供科学的理论指导,具有重要的理论意义和实用价值.
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