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Use of Neural Network to Model the Refractive Property of PECVD Silicon Nitride Films Used to Prevent Water Permeability of Piezoresistive Pressure Sensor

机译:使用神经网络对PECVD氮化硅薄膜的折射率进行建模,该薄膜用于防止压阻压力传感器的透水性

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The application of plasma-enhanced chemical vapor deposition (PECVD) silicon nitride, commonly used as the passivation layer for ICs, has been applied to advanced sensors as well. For example, the silicon nitride film coating the piezoresistive pressure sensor of a blood pressure sensor is used to prevent water permeation when immerged into liquid. In this paper, we applied a combination of design-of-experiment (DOE) and neural network to reveal the relationship between input parameters and refractive indexes of PECVD silicon nitride films. We also introduced a metrology to quantify the water permeability into the film based on the corresponding resistance test structure measurement and the break down measurement. These two performance tests are further used to optimize the silicon nitride deposition.
机译:通常用作IC钝化层的等离子增强化学气相沉积(PECVD)氮化硅的应用也已应用于高级传感器。例如,覆盖血压传感器的压阻压力传感器的氮化硅膜用于防止浸入液体中时水的渗透。在本文中,我们结合实验设计(DOE)和神经网络来揭示PECVD氮化硅膜的输入参数与折射率之间的关系。我们还引入了一种计量技术,可基于相应的电阻测试结构测量值和击穿测量值来量化膜中的水渗透率。这两个性能测试还用于优化氮化硅沉积。

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