Unsupervised learning for signal mapping in dynamic photon emission

Dynamic photon emission is an efficient tool for timing analysis of various areas. However, advances in transistors integration bring more complex test patterns and more objects to investigate. As a consequence, understanding the analyzed area and finding nodes of interest can be difficult. In this paper, a method for drawing synthesis of the various signals met inside an area is reported. It is based on unsupervised learning tool for dimension reduction and dustering. The process is applied to real data to show its efficiency and its quality is evaluated. (C) 2015 Elsevier Ltd. All rights reserved.