常用材料软X射线多层膜的全波段设计

利用薄膜光学理论及材料在软X射线波段的原子散射因子,设计了大批量的软X射线多层膜的计算程序,对常用材料的膜系配对进行了优化设计,给出的结果对软X 射线整个波段内的多层膜膜系的选择有一定的指导意义。     

软X射线多层膜光栅

详细介绍了软X射线多层膜光栅的国内外发展概况和广阔的应用领域，全面分析了它的制作工艺，并指出制作多层膜光栅的关键技术及解决方案。     

软X射线多层膜元件研究进展

对各国进行的软Ｘ射线多层膜研究所取得的最新成果给出了比较系统全面的评述，首次给出了文献报道的软Ｘ射线全波段的各种膜系实测正入射反射率的统计结果。     

长波长软X射线多层膜的设计与制备

“长波长软X射线多层膜的设计与制备”这篇论文介绍了用离子束溅射法制备长波长软X射线多层膜的研究工作,并报道了一种新的材料组合C/Si用于28.4nm(43. 65eV)和30.4nm(40.78 eV)波段的多层膜反射镜,并且用离子束溅射装置制备了正入 射条件下的C/Si多层膜反射镜.同时,用软X射线反射计测量了样品的反射率,从实验结果看出,制备的多层膜样品在28.4nm和30.4nm波段附近的实测正入射反射率分别达到11.4%和14.3%,实验指标达到了国内领先水平,并接近了国际水平.     

软X射线高反射率多层膜反射镜

日本松下技术研究所用准分子激光CVD工艺技术实现软X射线聚光用高反射率多层膜反射镜,这种反射镜是在硅基板上由钨(W)和硅(Si)交互沉积的多层膜(W/Si)构成椭圆柱面反射镜(尺寸:55×55mm~2,短半径60mm),(图1).多层膜的各层,在基板纵剖面内的膜厚是不同的.该椭圆形反射镜能把点光源发出的软X射线聚成光束.所发出的两条光线能聚焦成一点.     

C/Al软X射线多层膜反射镜

在λ=285nm波长处,选择了一种新的多层膜材料对C／Al。与Mo／Si,C／Si软X射线多层膜相比,正入射C／Al多层膜在150nm附近有很低的二级衍射峰。用磁控溅射法制备了C／Al软X射线多层膜样品,并用X射线小角衍射法对其结构进行了测试。     

不连续金属薄膜对短波长软X射线多层膜反射特性的影响

讨论了不连续金属薄膜对软Ｘ射线多层膜反射特性的影响。给出了理论模型，具体计算了在短波段的软Ｘ射线多层膜反射率与不连续金属岛状膜在基底表面覆盖率之间的关系，修正了文献中的评估方法，并建立了新的定性评估包含不连续金属膜层的软Ｘ射线多层膜光学特性分析模型     

软X射线多层膜反射镜界面粗糙度的一种估算方法

介绍了D.G.Sterns的散射方法,用它来描述单个非理想粗糙界面的散射,它适用于软X射线短波段区域.将这种方法应用到多层膜结构,并采用D.G.Sterns方法的数学模型来描述软X射线短波段区域(1～10 nm)多层膜界面粗糙度.在这个理论的框架下,对我们所研制的波长为4.77 nm的Co/C多层膜反射镜界面粗糙度进行分析,估算出该多层镜界面间均方根粗糙度为0.7 nm.粗糙度估算结果与小角X射线衍射的测定结果相一致.(PE13)     

45°角入射的13.1nm软X射线多层膜的研制

报道了对４５°入射角高反的１３．１ｎｍ软Ｘ射线多层膜反射镜的研制情况。利用在星光装置中进行的软Ｘ射线激光等离子体实验测量多层膜反射率的方法，获得了２６．２％的实测反射率，该反射率已达到理论反射率的７０％。     

小角衍射法精确测定Mo/Si软X射线多层膜的结构

通过对周期性Mo/Si多层膜Cu靶K_(?)线小角衍射曲线的分析,并进一步采用对局部曲线进行分步数值拟合的方法,确定了多层膜的结构参数及界面质量。估算了多层膜样品在λ=20nm附近的正入射反射率接近10％。     

制备软X光多层膜的转速控厚法

本文提出了一种新的监控软X光多层膜膜厚的方法——转速控厚法,利用这种方法镀制的设计周期厚度分别为8.4和10nm,周期数达50对和30对的W/C多层膜,经小角X光衍射测试,结果表明周期厚度随机误差在0.1nm左右。     

软X激光反射输出镜的近似理论设计

本文采用标量区近似理论设计软X激光反射输出镜,分析其衍射特性,对影响输出效率的因素进行了讨论和分析。     

多层介质涂覆结构的高阶张量阻抗边界条件

对于多层复杂介质涂敷的导体表面，用谱域法给出介质与空气界面处的谱域精确阻抗表达式，对将其变换至空域任意阶阻抗边界条件的方法的可行性进行分析，并引入优化方法，成功地得到空域任意阶张量阻抗边界条件。通过算例说明，对于多层复杂介质涂敷、低反差介质涂敷以及厚涂敷的情形，采用优化方法导出任意阶阻抗边界条件简单、可行。     

mSCTP for soft handover in transport layer

In this letter, we discuss mobile Stream Control Transmission Protocol (SCTP) for soft handover in the transport layer. From the experimentations on triggering rules for Add-IP and Primary-Change during the handover, it is shown that the aggressive Add-IP and conservative Primary-Change rules provide better throughput.     

Phase-matching techniques for coherent soft X-ray generation

Coherent beams at soft X-ray (SXR) wavelengths can be generated using extreme nonlinear optics by focusing an intense laser into a gas. In this paper, we discuss phase-matching and quasi-phase-matching techniques that use gas-filled modulated waveguides to enhance the frequency conversion process. This leads to the generation of SXR beams that are both spatially and temporally coherent.     

Amplified MOS imager for soft X-ray imaging

This paper describes a new area sensor for soft X-rays, and its performance. The operational principle is based on detecting the change in potential of a floating photodiode caused by X-ray-induced electron-hole pairs generation in a stacked amorphous silicon photoconversion layer. The photoresponse was measured at wavelength from 50 /spl Aring/ to 160 /spl Aring/. The signal to noise ratio of 25 dB was achieved, when the number of incident 70 /spl Aring/ X-ray photons is as low as 230/pixel. Quantum efficiency (stored carriers/photon) at 70 /spl Aring/ wavelength was 22. In addition, good reproducibility (<10% deviation) between different detectors and good reproducibility (<20% deviation) after ten months were also clarified. The performance of this area sensor indicates its potential for detection of soft X-ray images.<>     

低介电常数高耐热多层线路板用基板材料

Polyphenylene（聚酰亚胺）树脂带有非常好的介电性能,通过对Polyphenylene分子细量化,然后和带有好架桥性能以及可溶混性的树脂搅拌,开发出了同时带有低诱电（Low-Dk）和高耐热性,适用于高速信号传输设备使用的多层线路板材料。这个材料性能为Dk=3.8, Df=0.005(1 GHz), Tg=210℃。在5 GHz的使用条件下,与传统的材料相比,降低信号损失约15 db/m。可以广泛的应用在如网络设备等的高速,大容量信号传输设备。     

The use of multilayer neural networks in material synthesis

This paper demonstrates the incorporation of a multilayer neural network in semiconductor thin film deposition processes. As a first step toward neural network-based process control, we present results from neural network pattern classification and beam analysis of reflection high energy electron diffraction RHEED images of GaAs/AlGaAs crystal surfaces during molecular beam epitaxy growth. For beam analysis, we used the neural network to detect and measure the intensity of the RHEED beam spots during the growth process and, through Fourier transformation, determined the thin film deposition rate. The neural network RHEED pattern classification and intensity analysis capability allows, powerful in situ real time monitoring of epitaxial thin film deposition processes. Our results show that a three layer network with sixteen hidden neurons and three output neurons had the highest correct classification rate with a success rate of 100% during testing and training on 13 examples