本项目以含大范围滑动/旋转零部件MEMS失效的主要形式---侧壁面磨损为研究对象，以降低侧壁面的磨损为目标，提出将微纳米纹理与加工遗传的侧壁面微观形貌相耦合的策略，实现基于已有微纳米加工技术的高耐磨侧壁面的快速制备并与MEMS器件加工过程相融合。建立实验研究、分子动力学模拟与理论分析相结合的研究方法，开展基于微纳米纹理技术的硅侧壁面抗磨机理和抗磨设计方法研究。在研究微纳米纹理对侧壁面磨损性能影响规律的基础上，从纳米力学性能、磨损接触力学、磨损面损伤机制及磨料动力学四个方面揭示基于微纳米纹理技术的硅侧壁面抗磨机理。研究不同加载条件下微纳米纹理和侧壁面原有微观形貌几何参数对侧壁面磨损性能的影响规律，建立人工神经网络结合遗传算法的磨损性能优化设计方法，实现降低硅基MEMS侧壁面磨损的微纳米纹理设计，为促进含大范围滑动/旋转零部件MEMS的发展提供重要的基础理论支持。

This project is focused on the antiwear mechanism and design method of silicon sidewall using micro/nano-patter technology, as the wear at sidewall is the main failure mode of the MEMS including extensive sliding/rotating parts. In order to reduce wear at silicon sidewall, a strategy coupling the micro/nano-patter with the microtopography of sidewall formed by processing is proposed, which can be rapidly implemented by existing micro/nanoscale machining technology and can be merged into the processing of MEMS device. Based on the research of the effect of the micro/nano-patter on the wear property, the antiwear mechanism of the silicon sidewall using micro/nano-patter technology is investigated from nanomechanical properties, contact mechanics during wear, damage mechanism in wear surface and abrasive dynamics. Then, the influence of the geometric parameter of micro/nano-patter and the microtopography of sidewall on the wear property is systematicly studied under different loading conditions. And an optimized design method coupling artificial neural network with genetic algorithm is established in order to design the micro/nano-patter with low wear silicon sidewall. This work can provide theoretical support for developing the MEMS including extensive sliding/rotating parts.