An imaging performance analysis method correlated with geometrical deviation for the injection molded high-precision aspheric negative plastic lens

摘要

High-precision aspheric negative plastic lenses are widely used in optical systems owing to their excellent performance and ease of high-efficiency manufacturing. The imaging performance of the lens is difficult to control, because it is unable to perform optical measurements directly and is sensitive to manufacturing processing meanwhile. Generally, the imaging performance is guaranteed by a strict control of geometrical deviation, such as Peak-to-Valley (PV) and Root-Mean-Square (RMS). In this study, an optical ray-tracing algorithm with the measured geometrical deviation data is proposed to perform an imaging performance analysis correlated with geometrical deviation for the injection molded high-precision aspheric negative plastic lens. Taguchi experiments are applied to investigating the effect of processing parameters. The geometrical deviation of the convex surface is found to be an order of magnitude greater than that of the concave surface. The geometrical dimension of the concave surface is mainly determined by the machining precision of the mold cavity surface, whereas the convex surface dimension is mainly affected by the lens shrinkage. However, the imaging performance has a nonlinear correlation with the geometrical deviation. Modulation Transfer Function (MTF) and Spot Diagram are equivalently affected by the concave and convex geometrical deviations, and depend on the object field height. The effect of processing parameters on geometrical deviation and imaging performance is uncorrelated. Thus, the imaging performance should be simultaneously considered as a criterion as well as the geometrical deviation in the optimization of aspheric negative lens injection molding processing. The imaging performance prediction using an optical ray-tracing algorithm with the measured geometrical deviation data is instrumental to optimize the manufacturing processing.