Ba1-xSrxTiO3热释电梯度厚膜的制备和介电性能研究

钛酸锶钡(BST)组分梯度多层厚膜具有较好的综合介电性能,包括适中的介电常数、高的介电温度系数、低的介质损耗等,日益成为红外探测器、微波调制器件的重要候选厚膜材料.采用改进的溶胶-凝胶(Sol-Gel)法在Pt/Ti/SiO2/Si基片上制备Mn掺杂6层不同组分梯度BST厚膜.厚约5 μm.研究了梯度BST厚膜的微观结构及其介电性能.X-射线衍射(XRD)分析表明,当热处理温度为750℃时,得到完整钙钛矿结构的厚膜材料.扫描电镜(SEM)电镜显示,厚膜表面晶粒大小均匀,排列紧密,致密性好,梯度BST厚膜的介电峰温区覆盖常温,介电常数峰值为920,介电损耗约为1.8×10-2.     

两步加压法制备高介电调谐率的Ba_(0.6)Sr_(0.4)TiO_3厚膜

在传统的丝网印刷厚膜制备工艺中增加了两步加压预处理流程,采用这种新方法制备了致密的Ba0.6Sr0.4TiO3(BST)厚膜,并系统地研究了两步加压法对BST厚膜微结构和介电性能的影响。结果表明,与传统工艺相比,两步加压法可使BST厚膜获得更致密的微结构和更高的介电调谐率。在20℃和10kHz环境下,厚膜的介电常数和介电损耗分别为958和0.013 4,6kV/mm偏场下介电调谐率和优值分别为48.6%和36.3。     

B2O3-Li2O掺杂对丝网印刷Bao.6Sr0.4TiO3厚膜微观结构及介电特性的影响

采用丝网印刷工艺并通过有效掺杂B2O3-Li2O制备出了一致性和可重复性良好的Ba0.6Sr0.4TiO3(BST)厚膜.系统研究了B2O3-Li2O掺杂对BST厚膜的低温烧结特性、微观结构、相构成及介电性能的影响.结果表明:随着B2O3-Li2CO3含量的增加,BST厚膜的烧结温度逐渐降低,晶粒尺寸和介电常数显著变小...     

CeO_2缓冲层对蓝宝石基Ba_(0.6)Sr_(0.4)TiO_3薄膜结构和介电性能的影响

利用磁控溅射法制备Ce O_2缓冲层,通过脉冲激光沉积法制备Ba_(0.6)Sr_(0.4)TiO_3(BST)薄膜,在Al_2O_3(11—02)蓝宝石基片上构架了Pt/BST/Ce O_2/Al_2O_3和Pt/BST/Al_2O_3叉指电容器,对比研究了Ce O_2缓冲层对BST薄膜结构和叉指电容器介电性能的影响。通过X射线衍射仪、原子力显微镜和LCR表分别对叉指电容器的结构、表面形貌和介电性能进行了表征。实验发现,直接沉积在蓝宝石上的BST薄膜为多晶结构,生长在Ce O_2缓冲层上的BST为(001)取向的高质量外延薄膜。生长在Ce O_2缓冲层上的BST薄膜相对于没有缓冲层的BST薄膜具有更小的晶粒和均方根粗糙度。在40 V偏置电压下,Pt/BST/Al_2O_3和Pt/BST/Ce O_2/Al_2O_3叉指电容器的调谐率分别是13.2%和25.8%;最小介电损耗为0.021和0.014。结果表明Ce O_2缓冲层对生长在蓝宝石基片上的BST薄膜结构和介电性能具有重要影响。     

PZT叠层厚膜陶瓷材料性能的研究

使用凝胶流延成型工艺制备了PZT85/15、PZT95/5单组分厚膜及叠层厚膜.通过和单组分厚膜的性能进行对比,分析了叠层厚膜的形貌特征及介电、铁电和热释电性能.结果表明,由PZT85/15和PZT95/5组成的叠层厚膜内部均匀致密,具有良好的介电、铁电和热释电性能,极化后的相对介电常数εr=191.5,介电损耗tan δ=0.7%(温度T=20℃,频率f=1 kHz),热释电系数在41℃时达到峰值8×10-8 C/cm2·K,此后下降不明显,从而改善了单组分厚膜相变温区过窄的缺点,达到拓宽相变温区的目的,有望在红外探测和相变换能方面得到更广泛的应用.     

铁电（Ba,Sr）TiO3及介电Bi2O3-ZnO-Nb2O5薄膜材料应用于电调谐微波器件研究进展

铁电钛酸锶钡BaxSr1-xTiO3(BST)是一种拥有十分优越铁电/介电性能的材料,在可调谐微波器件方面具有很好的应用前景。本文概括介绍了BST薄膜的研究意义、基本结构、薄膜的制备方法,并针对可调谐微波器件应用需求,详细探讨了通过掺杂、组分梯度变化、纳米铁电多层薄膜以及将铁电BST与新型介电Bi2O3-ZnO-Nb2O5(BZN)薄膜相结合等对铁电薄膜性能进行优化的手段,最后对该领域的前沿问题从材料研究层面作了小结与展望。     

梯度多层BST薄膜介电性能研究

成分梯度多层钛酸锶钡薄膜具有较好的综合介电性能,包括适中的介电常数、高的介电调谐率、低的介质损耗及低的介电温度系数等,日益成为微波调制器件如移相器、滤波器、谐振器等的重要候选薄膜材料。就国内外近年来取得的重要成果进行了综述,对今后成分梯度多层BST薄膜的研究前景及方向进行了展望。     

磁控溅射法制备BST铁电薄膜的实验研究

采用射频磁控溅射法在Si和Pt/TiOx/SiO2/Si衬底上沉积了(Ba0.65Sr0.35)TiO3铁电薄膜,研究了BST铁电薄膜微观结构和介电性能。实验结果表明:衬底温度在550℃,工作气压为2.0 Pa的溅射条件下沉积的BST薄膜,经750℃退火处理30 min后,形成了完整的钙钛矿相;与Si衬底相比,在Pt衬底上制备的BST薄膜晶粒更均匀、表面平整无裂纹。在室温、频率为100 kHz条件下薄膜的介电常数ε=353.8,介电损耗tanδ=0.012 8。介电温谱结果表明制备的(Ba0.65Sr0.35)TiO3铁电薄膜居里温度在5.0℃左右。     

覆有BST膜的半导体功率器件结终端研究

通过一系列的工艺步骤,在半导体功率器件含有场限环(FLR)的结终端上覆盖了一层300 nm厚、介电常数高的钛酸锶钡( BST)膜.对该新型结终端和无BST膜的传统FLR结终端的结构与性能进行了研究比较.结果表明,在覆盖BST膜后,FRL结终端的结构击穿电压提高了50％.这证明BST膜能够提高器件的击穿电压.     

溶胶-凝胶法制备硼硅玻璃掺杂BST陶瓷的研究

研究了Si-B-O系玻璃掺杂对钛酸锶钡(BST)陶瓷的相结构和介电性能的影响.实验结果表明,当x(SiO_2)＞10%时,Si-B-O系玻璃掺杂BST陶瓷易出现杂相,即Ba_2TiSi_2O_8相.x(SiO_2)≤10%,Si-B-O系玻璃掺杂BST陶瓷粉体的相结构为立方钙钛矿相结构,其合成温度大于等于600 ℃,不存在第二相. Si-B-O系玻璃掺杂BST陶瓷的烧结温度低于传统工艺.Si-B-O系玻璃掺杂BST陶瓷的显微结构呈细晶结构(晶粒尺寸＜1 μm).随玻璃含量的增加,Si-B-O系玻璃掺杂BST陶瓷介电常数ε降低,介电峰变低,平坦,峰形宽化,介电损耗降低,居里温度TC向低温移动.     

Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea

A cardiorespiratory-based automatic sleep staging system for subjects with sleep-disordered breathing is described. A simplified three-state system is used: Wakefulness (W), rapid eye movement (REM) sleep (R), and non-REM sleep (S). The system scores the sleep stages in standard 30-s epochs. A number of features associated with the epoch RR-intervals, an inductance plethysmography estimate of rib cage respiratory effort, and an electrocardiogram-derived respiration (EDR) signal were investigated. A subject-specific quadratic discriminant classifier was trained, randomly choosing 20% of the subject's epochs (in appropriate proportions of W, S and R) as the training data. The remaining 80% of epochs were presented to the classifier for testing. An estimated classification accuracy of 79% (Cohen's kappa value of 0.56) was achieved. When a similar subject-independent classifier was trained, using epochs from all other subjects as the training data, a drop in classification accuracy to 67% (kappa = 0.32) was observed. The subjects were further broken in groups of low apnoea-hypopnea index (AHI) and high AHI and the experiments repeated. The subject-specific classifier performed better on subjects with low AHI than high AHI; the performance of the subject-independent classifier is not correlated with AHI. For comparison an electroencephalograms (EEGs)-based classifier was trained utilizing several standard EEG features. The subject-specific classifier yielded an accuracy of 87% (kappa = 0.75), and an accuracy of 84% (kappa = 0.68) was obtained for the subject-independent classifier, indicating that EEG features are quite robust across subjects. We conclude that the cardiorespiratory signals provide moderate sleep-staging accuracy, however, features exhibit significant subject dependence which presents potential limits to the use of these signals in a general subject-independent sleep staging system.     

Computer-assisted sleep staging

To address the subjectivity in manual scoring of polysomnograms, a computer-assisted sleep staging method is presented in this paper. The method uses the principles of segmentation and self-organization (clustering) based on primitive sleep-related features to find the pseudonatural stages present in the record. Sample epochs of these natural stages are presented to the user, who can classify them according to the Rechtschaffen and Kales (RK) or any other standard. The method then learns from these samples to complete the classification. This step allows the active participation of the operator in order to customize the staging to his/her preferences. The method was developed and tested using 12 records of varying types (normal, abnormal, male, female, varying age groups). Results showed an overall concurrence of 80.6% with manual scoring of 20-s epochs according to RK standard. The greatest amount of errors occurred in the identification of the highly transitional Stage 1, 54% of which was misclassified into neighboring stages 2 or Wake.     

Respiration-movement-based sleep apnea monitor

An electronics hardware-based monitor is presented for the detection and differentiation of all forms of sleep apnea solely by the information from abdominal and thoracic excursion signals. The working of the developed monitor has been successfully tested by generating the conditions of various forms of apnea artificially.     

Optimization of sleep period in watchful sleep mode for power-efficient passive optical networks

The energy consumption in data communication networks has drawn global attention due to the ever-increasing of broadband users. In this paper, we formulated the system performances of the watchful sleep mode in terms of the power consumption and the state transition delay by using the state probability based on arrival traffic profile. And we compared the performances of the watchful sleep mode with the cyclic sleep mode under the same conditions. For above two conflicting performance indexes, the Sleep period is a key factor. Thus we designed the Cost function to determine the balanced Sleep periods for the certain requirements of power saving and state transition delay. And the simulation results verified the balanced Sleep period can greatly improves the system performances.     

在梦中约会音乐

每天午间的固定约会，拒绝接见客户，拒绝答题算术，夜晚和枕头约会，就让助眠的催眠曲来锦上添花。     

Noncontact method for sleep stage estimation

This paper describes a novel method to estimate sleep stage through noninvasive and unrestrained means. The Rechtschaffen and Kales (R-K) method is a standard to estimate sleep stage. However, it involves restraining the examinee and, thus, induces psychological stress. Furthermore, it requires specialists with a high degree of technical expertise and the use of an expensive polygraph. The sleep estimation method presented here is based on the noninvasive and unrestrained pneumatic biomeasurement method presented by the authors. Sleep stage transition in overnight sleep and the relationship between sleep stage and biosignals measured using the pneumatic method was analyzed and from the results, a mathematical model of sleep was created. Based on this model, a sleep stage estimator, including a sleep stage classifier and observer, was designed. The sleep state transition equation was the basis for the design of this observer, while the observed relationships were the basis for designing a classifier. Agreement of the estimated sleep stages with those obtained using the R-K method for the non-REM stage was 82.6%, for the REM stage was 38.3 % and for Wake was 70.5 %, including disagreement. However, the new method might ultimately result in better estimation of sleep stage due to the fact that it does not physically restrain the patient and does not induce psychological stress.     

多传感器数据融合睡眠监测系统设计

鉴于当前的睡眠监测设备存在佩戴复杂、设备昂贵、功能单一和检测方法不合理等情况,设计了一种多传感器数据融合的睡眠监测系统.本系统以STM32F407ZGT6为核心主控芯片,通过可穿戴式背心传感器将所采集到的人体特征信号进行处理、存储和发送到手机App端,针对单一传感器检测可靠性较差的特点,本系统采用了多传感器进行采集分析.经过测试表明,该系统实现了常用睡眠参数的准确监测.     

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.