Low-temperature poly-SiGe alloy growth of high gain/speed pin infrared photosensor with gold-induced lateral crystallization (Au-ILC)

The hydrogenated poly-silicon germanium (poly-SiGe:H) epitaxial film has been investigated using gold-induced lateral crystallization (Au-ILC) technology on a-SiGe:H layers at 10-h 350/spl deg/C annealing temperature and 60-sccm hydrogen (H/sub 2/) content. Using this optimal condition, the growth rate of the induced Au was as large as 15.9 /spl mu/m/h. With a low annealing temperature (/spl les/400/spl deg/C) and large growth rate, this novel technology will be noticeably useful for poly-SiGe:H pin IR-sensing fabrication on a conventional precoated indium tin oxide (ITO)-glass substrate. Under a 1-/spl mu/W IR-LED incident light (with peak wave length at 710 nm) and at a 5-V biased voltage, the poly-SiGe:H pin IR sensor developed by the Au-ILC technology, i.e., an Al (anode)/n poly-SiGe:H/i poly-SiGe:H/p poly-SiGe:H/ITO (cathode)/glass-substrate structure allowed for maximum optical gain and response speed. The optical gains and the response speeds were almost 600 and 130%, respectively, better than that of a traditional pin type. Meanwhile, the FWHM of a poly-SiGe:H pin sensor with Au-ILC technology was reduced from 280 to 150 nm. This reveals excellent IR-sensing selectivity. These IR-sensing trials demonstrated again that the proposed Au-ILC technology has very useful application in the field of low cost integrated circuits on optoelectronic applications.     

化学复合镀层激光处理研究

研究了激光处理对Ni-P -SiC化学复合镀层的影响。借助于扫描电镜、能谱仪、X射线衍射、显微硬度计等设备对激光处理后复合镀层的表面形貌、组织结构及性能进行了综合分析。结果表明 ,对复合镀层进行激光处理可以获得与炉内加热同样的镀层硬度 ,且当激光功率 4 0 0W ,扫描速度 1.5m /min时 ,镀层硬度高于炉内加热的硬度     

单元串联式多电平高压变频器的起源、现状和展望

本文对单元串联式多电平高压变频器的起源和现状进行了总结,同时从无速度传感器矢量控制、大容量化、冗余设计等方面对该技术未来的发展趋势进行了展望。     

CSTN-LCD用铬版清洗效果的改善

利用铬版清洗机对CSTN-LCD用铬版进行清洗处理,分析了清洗过程中出现的铬层脱落现象,铬层脱落与轴向喷射压力和水平切力密切相关,通过调整转速和喷射压力探索了安全使用条件:在转速不超过1000r/min,喷射压力低于0.5×105Pa的情况下使用铬版清洗机进行清洗是相对安全的。同时采用预清洗处理的方法满足清洁要求,使换线时间由平均3h缩短到平均1h以内,年产能提高270000片。     

井下乳化/水热催化裂解复合降黏开采稠油技术研究

实验考察了胜利孤东稠油井下催化水热裂解和乳化/催化水热裂解降黏效果。所用催化剂为水溶性铁镍钒体系,Fe3+∶Ni2+∶VO4+=5∶1∶1,100 g稠油与30 g 0.5%催化剂水溶液在240℃反应24小时。原始黏度(50℃)11.0和8.36 Pa.s的两种稠油裂解并静置除水后,黏度降低76.2%和75.6%,室温放置60天后降黏率下降小于3个百分点,气相色谱显示裂解后轻组分明显增加,红外光谱显示稠油组分发生脱羧反应且芳环数减少。讨论了稠油催化水热裂解反应机理。所用化学助剂JN-A在油水中均可溶,耐温达250℃,耐矿化度达50 g/L,其水溶液以30∶100的质量比与稠油混合时形成低黏度的O/W乳状液。当水相含1.0%JN-A和0.5%催化剂时,两种稠油水热裂解后的反应混合物为O/W乳状液,黏度仅为319和309 mPa.s,静置除水后的稠油降黏率增加到86.5%和87.3%,其中的轻组分含量进一步增加。该井下乳化/催化水热裂解复合降黏法成功地用于孤东两口蒸汽吞吐井,稠油井作业后初期采出的原油黏度由~9 Pa.s降低到1 Pa.s左右,随采油时间延长而逐渐升高,约50天后超过4Pa.s。图2表6参5。     

基于扩展分形和CFAR特征融合的SAR图像目标识别

研究了多信息融合技术在SAR图像目标识别中的应用。将扩展分形特征(Extended Fractal)与双参数恒虚警特征(Double Parameter CFAR)形成的多信息进行融合处理。运用Dempster-Shafer证据理论，在决策层对SAR图像中的像素进行识别分类。实验结果表明通过融合对像素分类的准确性明显好于单特征的检测结果，减少了虚警概率，提高了系统的识别能力。     

运营商内部数据通信网的VPN技术

针对电信运营商在构建内部数据通信网(DCN)时遇到的各应用系统的隔离问题，详细介绍了目前几种主流的虚拟专用网(VPN)技术，并进行分析比较，最后提出了运营商内部数据通信网的VPN解决方案。     

机械合金化Fe-Ni粉末的相结构

使用XRD和Moessbauer等方法，研究了在Ar气氛下机械合金化Fe—Ni粉末相结构的变化．结果表明，在机械合金化Fe64-Ni36粉末过程中，fcc相的数量随着球磨时间的增加先增加然后减少，与加乙醇球磨Fe64-Ni36的情形相同．当Ni的含量(原子分数)大于50％时，有fcc相、顺磁相和FeNi3形成，当Ni的含量低于50％时，bcc相的数量随着Ni含量减少而增加．Moessbauer谱的结果表明，因球磨时间或Fe、Ni比例的不同，Fe—Ni球磨粉末固溶体具有不同结构的原子配比。     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.