Method to budget and optimize total device overlay

We combine lithographic simulation, experimental data and statistical modeling to build a predictive estimator of total device overlay. To generate accurate predictions of total overlay, we include error estimates on lens image placement, CD control, reticle and exposure tool alignment. Instead of combining these errors in ad hoc root sum square fashion to make overlay estimates, we construct a physical model of the device and metrology marker edge placement processes. The model comprehends the differential placement of metrology structures and device features due to lens and illumination system asymmetries and is therefore applicable to the evaluation of arbitrary illumination and pattern geometry conditions. Since we attempt to model the relative placement distribution of specific device features, the model produces overlay estimates that are directly relevant for device performance. The comparison of our total overlay estimate to device overlay sensitivity data allows a projection of the overlay related yield loss for a given device, process and tools et. Finally, our model allows the process engineer to made informative choices on the optimum error sources to pursue for improving overlay.