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Basic image intensity parameters, like maximum and minimum intensity values (Imin and Imax), image logarithm slope (ILS), normalized image logarithm slope (NILS) and mask error enhancement factor (MEEF) , are well known as indexes of photolithography imaging quality. For full chip verification, hotspot detection is typically based on threshold values for line pinching or bridging. For image intensity parameters it is generally harder to quantify an absolute value to define where the process limit will occur, and at which process stage; lithography, etch or post- CMP. However it is easy to conclude that hot spots captured by image intensity parameters are more susceptible to process variation and very likely to impact yield. In addition these image intensity hot spots can be missed by using resist model verification because the resist model normally is calibrated by the wafer data on a single resist plane and is an empirical model which is trying to fit the resist critical dimension by some mathematic algorithm with combining optical calculation. Also at resolution enhancement technology (RET) development stage, full chip imaging quality check is also a method to qualify RET solution, like Optical Proximity Correct (OPC) performance. To add full chip verification using image intensity parameters is also not as costly as adding one more resist model simulation. From a foundry yield improvement and cost saving perspective, it is valuable to quantify the imaging quality to find design hot spots to correctly define the inline process control margin. This paper studies the correlation between image intensity parameters and process weakness or catastrophic hard failures at different process stages. It also demonstrated how OPC solution can improve full chip image intensity parameters. Rigorous 3D resist profile simulation across the full height of the resist stack was also performed to identify a correlation to the image intensity parameter. A methodology of post-OPC full chip verification is proposed for improving OPC quality at RET development stage and for inline process control and yield improvement at production stage.
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