Research Center for Informatics,
Indonesian Institute of Sciences (LIPI), Indonesia
Esa Prakasa received B.Eng. in Nuclear Engineering (1998) and M.Eng. in Electrical Engineering (2001) from Universitas Gadjah Mada, Indonesia. He obtained his PhD in Electrical and Electronic Engineering (2014), from Universiti Teknologi PETRONAS, Malaysia. He is a researcher at Research Center for Informatics, Indonesian Institute of Sciences (LIPI) since 2005.
His research interests are computer vision, 3D medical imaging, visual inspection, and pattern recognition. He has been involved in some research projects related with digital image processing and pattern recognition for various applications. He has contributed to develop an imaging based technique for assessing psoriasis skin disease. The image analysis method was applied to quantify diseases severity based on several skin lesion properties. He has conducted an intensive collaboration research with dermatologists at Dermatology Department, Hospital Kuala Lumpur, Malaysia. The research project was conducted from 2008 to 2013 and have successfully recruited around 200 psoriasis patients. Since 2014, he has been involved in a collaborative project between LIPI and CERN, Switzerland. He contributed to develop visual inspection methods for assessing sensor chip qualities at production and detector construction stages. The detector will be used in ALICE (A Large Ion Collider
Experiment) Project, one of the largest physic experiments in the world hosted by CERN. He has also successfully secured two years research grant from The Ministry of Research, Technology, and Higher Education, Republic of Indonesia for conducting research project on wood identification by using computer vision methods. The research is a collaborative project between LIPI and Forest Products Research and Development Center, Ministry of Environment and Forestry, Indonesia. He has published papers in journals, international conferences, book chapters, and patents.
Keynote Title: Development of Vision-based Algorithms for Inspecting Chip Quality
A large hadron collider is operated by CERN (the European Organization for Nuclear Research) to conduct a large scale and a long term particle physic experiment, namely ALICE (A Large Ion Collider Experiment). Thousand of sensor chips are used in the collider sensor system to track the particle trajectories. Vision-based algorithms are proposed to assess the quality of sensor chips. The chip quality is defined by measuring several physical parameters. The parameters include the edge thickness of pad surface, edge cutting integrity, surface defects, and chip alignment. The 3D imaging algorithm has been developed to measure the edge thickness of pad surface. A 3D scanner based on structure light projection method is used to scan the chip surfaces. Edge cutting integrity is observed to ensure there is no crack path along the cutting profile of the chip. The surface defects is also examined to find damage region on the pad surface. Subsequent step of the production stage is also presented in this talk. The step is known as chip alignment. In this step, sensor chip position should be adjusted to fit with circuit board of the sensor. The algorithms have been tested by using chip images at microscopic level. The experiment results show that the vision-based algorithm can provide consistent measurement for the observed quality parameters.