Conference proceeding
Knowledge-based design of optoelectronic packaging and assembly automation
Proceedings of SPIE, v 5264(1), pp 283-294
02 Oct 2003
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper, we present an automation technique that yields high performance, low cost optoelectronic alignment and packaging through the use of intelligent control theory and system-level modeling. Our control loop design is based on model predictive control, previously popularized in process and other control industries. Our approach is to build an a priori knowledge model, specific to the assembled package"s optical power propagation characteristics, and use this to set the initial "feed-forward" conditions of the automation system. In addition to this feed-forward model, our controller is designed with feedback components, along with the inclusion of a built in optical power sensor. The optical modeling is performed with the rigorous scalar Rayleigh-Sommerfeld formulation, efficiently solved using an angular spectrum technique. One of the benefits of using our knowledge based control technique is that the efficiency of the automation process can be increased, as the number of alignment steps can be greatly reduced. An additional benefit of our technique is that it can reduce the possibility that attachment between optical components will occur at local power maximums, instead of the global maximum of the power distribution. Therefore, our technique improves system performance, while reducing the overall cost of the automation process.
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2 citations in Scopus
Details
- Title
- Knowledge-based design of optoelectronic packaging and assembly automation
- Creators
- Timothy P Kurzweg - Drexel UniversityAllon Guez - Drexel UniversityShubham K Bhat - Drexel University
- Publication Details
- Proceedings of SPIE, v 5264(1), pp 283-294
- Conference
- Optomechatronic Systems IV, 4th
- Publisher
- Society of Photo-Optical Instrumentation Engineers (SPIE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000187162300033
- Scopus ID
- 2-s2.0-1242342935
- Other Identifier
- 991019168429604721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Instruments & Instrumentation
- Optics