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A MACHINE-VISION BASED APPROACH TO LOW-COST OPTOELECTRONIC PACKAGING

机译:基于机器视觉的低成本光电包装方法

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At present, the high cost of optoelectronic (OE) devices is largely due to the labor-intensive processes involved with packaging. Automating the packaging processes should result in a significant cost reduction. One of the most labor-intensive steps is aligning and attaching the fiber to the OE device, the so-called pigtailing process. Three low-cost machines were designed and built under an ARPA-funded project to perform sub-micron alignments and attachments of single-mode fibers to different OE devices. These Automated Fiber Pigtailing Machines (AFPMs) are compatible with a manufacturing environment and have a modular design for standardization of parts, and machine vision for maximum flexibility. The vision system greatly reduces mechanical fixturing constraints by requiring the critical components to be positioned only within the field of view of the camera. Object-recognition algorithms written by LLNL allow the AFPM to determine the initial locations of the OE device and the fiber. The performance goals of the AFPM are to perform each pigtailing operation in less than 3 minutes (including the epoxy curing time) and to operate unattended for up to 1 hour. The modular nature of the AFPM means that each machine may be easily customized for a particular application. This work was a collaboration among Uniphase Telecommunications Products (formerly United Technologies Photonics, UTP), Ortel, Newport Corporation, the Massachusetts Institute of Technology Manufacturing Institute (MIT), and Lawrence Livermore National Laboratory (LLNL).
机译:目前,光电(OE)器件的高成本主要是由于包装涉及的劳动密集型工艺。自动化包装过程应导致大幅降低。最具劳动密集型步骤之一是对准并将光纤连接到OE器件,所谓的猪头化工艺。设计并在ARPA资助项目下设计和构建了三台低成本机器,以对不同的OE器件执行子微米对齐和单模纤维的附件。这些自动化纤维尾灯机(AFPMS)与制造环境兼容,具有模块化设计,可用于零件的标准化,以及用于最大柔韧性的机器视觉。视觉系统通过仅需要仅在相机视野范围内定位的关键部件来大大减少机械固定限制。 LLNL编写的对象识别算法允许AFPM确定OE设备和光纤的初始位置。 AFPM的性能目标是在不到3分钟的时间(包括环氧固化时间)的每个尾纤操作,并无人看管最多1小时。 AFPM的模块化性意味着可以为特定应用容易地定制每种机器。这项工作是uniphase电信产品(以前的United Technologies Photonics,Utp),Ortel,Newport Corporation,Massachusetts Technoloctions Institute(麻省理工学院)和Lawrence Livermore国家实验室(LLNL)之间的合作。

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