Studies on the properties of integrated CMOS image sensor arrays on single crystalline thinned and flexible silicon chips
单晶薄化柔性硅芯片集成CMOS图像传感器阵列性能研究
基本信息
- 批准号:149710363
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2009
- 资助国家:德国
- 起止时间:2008-12-31 至 2013-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Silicon based CCD and CMOS image sensors are ubiquitous nowadays and are employed in different areas like cameras and cell phones. These sensors differ in size and resolution depending on their application but because of production reasons all are planar and rigid because of their thickness of 500-800 µm. However, only a layer of some micrometers on the top of the chip is necessary for the sensory function so that by removing of material of the backside to a remaining thickness of below 30 µm they become mechanically flexible. Thereby it is possible to apply them to a cylindrical surface by what a 360° angle view sensor can be implemented. This would be of great interest for applications in endoscopy.In the course of the first project phase extensive experience in circuit design, chip thinning, electrically and mechanically contacting of thinned chips and in measurement techniques has been gained. The influence of mechanical stress caused by bending of thinned sensors on several devices - as photo diodes, transistors and capacitors - under different substrate orientation was systematically examined, based thereon circuit concepts for the minimization of the bending dependence were developed and their suitability was verified using a pixel read-out circuit employing these concepts. In the course of the research new questions have arisen and a clarification will allow for an extensive comprehension of the behavior and of the optimal design of CMOS integrated image sensors on flexible substrates subjected to bending. Firstly, the influence of mechanical stress on the noise characteristics of photo diodes and transistors shall be studied as noise is limiting image sensor performance especially under low light conditions. Furthermore it shall be examined if a bandgap reference voltage source that is optimized regarding its voltage stability under mechanical stress can be designed by employing the design rules developed in the first project phase because the supply of a stable reference voltage is necessary for the functioning of many analog circuits. Furthermore, it shall be examined if the stress dependence of transistors can be reduced by modifying the gate and channel geometry and what influence the chip thickness has on the photodiode dark current. The suitability of thinned and flexible image sensors for new camera concepts shall be confirmed on an endoscope like demonstrator, where the sensor is mounted on the outside of a cylindrical carrier.
如今,基于硅的CCD和CMOS图像传感器已无处不在,并在摄像机和手机等不同区域使用。这些传感器的尺寸和分辨率不同,具体取决于它们的应用,但由于生产原因,所有传感器的厚度为500-800 µm,所有这些传感器都是平面和刚性的。但是,对于感觉函数,只有一层芯片顶部的某些微米是必需的,因此,通过将背面的材料拆除至30 µm以下的剩余厚度,它们变得机械柔性。因此,可以通过可以实现360°角度视图传感器将它们应用于圆柱体表面。对于内窥镜检查中的应用,这将引起极大的兴趣。在第一个项目阶段在电路设计方面的丰富经验,芯片稀疏,电力和机械接触稀薄的芯片以及测量技术中的接触。系统检查了在不同底物方向下弯曲的传感器在多个设备上弯曲(如照片二极管,晶体管和电容器)引起的影响,基于其基于其最小化弯曲依赖性的电路概念,并使用这些概念使用Pixel读出的电路来开发其适用性。在研究过程中,出现了新问题,澄清将使对CMOS集成图像传感器的最佳设计以及对经受弯曲的柔性基材的最佳设计进行广泛理解。首先,机械应力对照片二极管和晶体管噪声特性的影响应进行研究,因为噪声是限制图像传感器的性能,尤其是在低光条件下。此外,应检查是否可以通过使用在第一项项目阶段制定的设计规则来设计有关其在机械应力下的电压稳定性进行优化的带隙参考电压源,因为稳定参考电压的供应对于许多模拟电路的运行是必需的。此外,应检查是否可以通过修改栅极和通道几何形状来减少晶体管的应力依赖性以及芯片厚度对光电二极管暗电流的影响。稀薄且灵活的图像传感器对新相机概念的适用性应在像演示器这样的内窥镜上确认,其中传感器安装在圆柱形载体的外部。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Investigations on an Ultra-Thin Bendable Monolithic Si CMOS Image Sensor
超薄可弯曲单片 Si CMOS 图像传感器的研究
- DOI:10.1109/jsen.2013.2254474
- 发表时间:2013
- 期刊:
- 影响因子:4.3
- 作者:G. Dogiamis;B. Hosticka;A. Grabmaier
- 通讯作者:A. Grabmaier
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Professor Dr. Anton Grabmaier其他文献
Professor Dr. Anton Grabmaier的其他文献
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基于深度选择性光电容积描记法的单一测量位置脉冲传输时间测量方法 (DeePPG)
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- 批准号:
278868304 - 财政年份:2015
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Design and implementation of monolithic integrated electronic devices for the control and recordingof bidirectional multi-electrode arrays.
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143692466 - 财政年份:2011
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75465911 - 财政年份:2008
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基于 SPAD 的 LiDAR 数据处理的机器学习方法
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508688689 - 财政年份:
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