In development of optical proximity correction (OPC) for new technology nodes, optimization of assist features requires
multiple placement scenarios for each line/space or hole combination. Additionally, illumination and process conditions
are varied to determine the optimal process window. Under some illumination and process conditions, optimal printing
of the desired features is attained; however, undesired printing of sidelobes or sub-resolution assist features (SRAFs) also
occurs. Currently, CD sizes are measured for the desired feature and images are hand checked for unwanted features
(sidelobe or SRAF printing). This takes a large amount of time, given the hundreds to thousands of CDSEM
measurements required to generate a given OPC model. This problem is multiplied if several passes of data collection
are needed to optimize each OPC model and each layer. An automated method has been developed to quickly screen a
large number of SEM images for unwanted features, and if they exist, flag the measurement point so it can be easily
identified as an undesirable area of the process window. This method employs edge placement measurement capabilities
available with automated SEM recipe generation software to identify the presence of an unwanted feature within a given
image. A simple Boolean filter is used to exclude this process area as SRAF or sidelobe printing process space so it may
be excluded from the OPC model and from the operational process space. This automated method for identification of
SRAFs or sidelobes provides significant engineering time savings and allows characterization of the onset of undesirable
features to assist in optimization of OPC within a given process window.
With the increased use of attenuated phase shift masks, high NA, and highly light sensitive resists, accurate printing of both rectangular holes and square holes concurrently becomes an increasing challenge. Because of the relative difference in the amount of light passing through a rectangular hole compared to a square hole, printing both with good fidelity, and without sidelobing of the rectangular structures, is challenging. Sidelobes can arise when the first order of diffracted light from neighboring structures constructively interferes with light from the 6% attenuated background. Optimization of process conditions for square hole printing often results in sidelobing in rectangular structures on the same chip. Common methods for reducing sidelobes are to increase mask bias or partial coherence. Contrast considerations and mask inspectibility requirements limit the mask bias that can be used for square holes, and increasing partial coherence reduces the depth of focus for isolated square holes. This paper presents simulation and experimental results showing the effects of illumination conditions and mask bias on overall process window, including the sidelobe margin for rectangular hole structures. Sidelobe printing in rectangular holes is found to be extremely pitch dependent, and relatively insensitive to width. As discussed in this paper, the optimal process window depends on many factors, including the layout (of squares as well as rectangles), resist choice, scanner aberrations (coma), and illumination conditions. As with many aspects of photolithography, development of an optimal process requires consideration of all factors and making specific tradeoffs to reach this goal.
In this paper we present a method that optimizes the OPC model generation process. The elements in this optimized flow include: an automated test structure layout engine; automated SEM recipe creation and data collection; and OPC model anchoring/validation software. The flow is streamlined by standardizing and automating these steps and their inputs and outputs. A major benefit of this methodology is the ability to perform multiple OPC "screening" refinement loops in a short time before embarking on final model generation. Each step of the flow is discussed in detail, as well as our multi-pass experimental design for converging on a final OPC data set. Implementation of this streamlined process flow drastically reduces the time to complete OPC modeling, and allows generation of multiple complex OPC models in a short time, resulting in faster release and transfer of a next-generation product to manufacturing.