Optimal color weighting (OCW) is a promising technique to improve accuracy and robustness of alignment mark measurement in the lithography process. Measurement based OCW shows remarkable improvement of overlay error under laboratory conditions. In those conditions, one of the wafer processes is split and a simple form of mark deformation is present. However, the OCW effect has not been confirmed in the case of on-product overlay since various of deformations are mixed in the real FAB. We perform simple simulation showing that mixed deformations can deteriorate the performance of OCW and show that utilizing spatial characteristics of the wafer with OCW further improves the overlay error. As a result, we have improved on-product-overlay form 3.78 nm to 3.51 nm or 7.1% using data of 86 lots, 268 wafers in DUV layer of 3 nm logic device.
The random error has been increased relative to the systematic error in overlay misalignment, as the Critical Dimension(CD) of semiconductor-design shrinks to under the 20 nm on DRAM and single-digit nanometer on Logic. The random error comprises diverse factors including non-lithography context, which caused by intricate process other than the scanner itself, hence it’s hard to control through conventional control methods using control knobs of scanner . In this study, we show that how effectively control and reduce on product overlay(OPO) error through making the most use of the conventional control knobs aided by machine learning. In addition to showing improved results, we address that conventional overlay feedback control with weighted moving average(WMA) can give rise to fluctuation of OPO error over entire wafer area, especially on the edge of wafer, due to the lack of control capability or flexibility. As a result, we show that 15.7% of OPO error can be trained and predicted for in-fab data and OPO has been improved from 2.29 nm to 2.08 nm or 9.2% on average over 5-steps of 1,201 lots with simulator.
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