氧化铝颗粒的表面改性及其在C平面(0001)蓝宝石衬底上的化学机械抛光(CMP)性质

为了提高氧化铝颗粒的CMP性能, 本工作探索了一种合适的改性方法。同时, 为了改善其化学机械性能, 通过与其表面羟基的硅烷化化学反应和与Al和仲胺的络合两种作用, 用N-(2-氨基乙基)-3-氨基丙基三甲氧基硅烷表面改性氧化铝颗粒。本工作给出了化学反应机理, 即N-(2-氨基乙基)-3-氨基丙基三甲氧基硅烷接枝到氧化铝表面。通过傅里叶变换红外光谱(FTIR)和X射线光电子能谱(XPS)表征了改性氧化铝颗粒的组成和结构。结果表明: N-(2-氨基乙基)-3-氨基丙基三甲氧基硅烷已被成功地接枝到氧化铝颗粒的表面, 导致改性比未改性的氧化铝颗粒具有更好的化学和机械性能。测试了未改性和改性的氧化铝颗粒在蓝宝石基底上的CMP性能。结果显示: 改性氧化铝颗粒比未改性氧化铝颗粒有更高的材料去除速率和更好的表面质量。即, 改性氧化铝颗粒在pH=10时比未改性氧化铝颗粒在pH=13.00时表现出更高的材料去除率, 这将为减少设备腐蚀提供新思路。     

面向回转体零件的分类编码系统的设计

零件分类编码系统是实现派生式或半生成式CAPP的核心部分。本文针对回转体零件研究了零件分类编码系统构成的关键技术：结构形式和标识方法。应用特征技术建立了一个以每一个工艺特征为核心的柔性零件分类编码系统——SmartCode系统。     

HEMD: a highly efficient random forest-based malware detection framework for Android

Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google’s Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection.