Microlens under melt in-line monitoring based on application of neural network automatic defect classification

Abstract

The usage of convolutional neural networks (CNN) on images is spreading into various topics in lot of industries. Today in the semiconductor industry CNN are used to perform Automatic Defect Classification (ADC) on SEM review images in almost real time and with level of success as high as trained operators can do or more [1,2]. The possibilities to get new kind of information from images offer to engineers multiple potential usages. In this paper we propose to present derivatives usages of CNN applied to the CD-SEM metrology with specific focus on an application to detect undermelted microlens in our imager process flow [3]. CD-SEM metrology is used to perform Critical Dimension (CD) measurement on almost all patterning steps in the wafer cycle (after lithography and after etch). CNN allows us to get more information from pictures than only dimensions measured by the CD-SEM used to feed a control card. In our imager process flow we have steps to form microlenses. The microlens process fabrication consists in a first lithography step where microlens matrix is defined in resist. The result is a matrix of quite square parallelepipoid microlenses followed by a melting step in order to reflow resists and eventually form microlens with spherical cap shape. The figure 1 shows the evolution of microlens shape in function of melting process time.