Chun-Suk Suh

A simplified reaction-diffusion system of chemically amplified resist process modeling for OPC

As semiconductor manufacturing moves to 32nm and 22nm technology nodes with 193nm water immersion lithography, the demand for more accurate OPC modeling is unprecedented to accommodate the diminishing process margin. Among all the challenges, modeling the process of Chemically Amplified Resist (CAR) is a difficult and critical one to overcome. The difficulty lies in the fact that it is an extremely complex physical and chemical process. Although there are well-studied CAR process models, those are usually developed for TCAD rigorous lithography simulators, making them unsuitable for OPC simulation tasks in view of their full-chip capability at an acceptable turn-around time. In our recent endeavors, a simplified reaction-diffusion model capable of full-chip simulation was investigated for simulating the Post-Exposure-Bake (PEB) step in a CAR process. This model uses aerial image intensity and background base concentration as inputs along with a small number of parameters to account for the diffusion and quenching of acid and base in the resist film. It is appropriate for OPC models with regards to speed, accuracy and experimental tuning. Based on wafer measurement data, the parameters can be regressed to optimize model prediction accuracy. This method has been tested to model numerous CAR processes with wafer measurement data sets. Model residual of 1nm RMS and superior resist edge contour predictions have been observed. Analysis has shown that the so-obtained resist models are separable from the effects of optical system, i.e., the calibrated resist model with one illumination condition can be carried to a process with different illumination conditions. It is shown that the simplified CAR system has great potential of being applicable to full-chip OPC simulation.

Comparison of OPC models with and without 3D-mask effect

OPC models with and without thick mask effect (3D-mask effect) are compared in their prediction capabilities of actual 2D patterns. We give some examples in which thin-mask models fail to compensate the 3D-mask effect. The models without 3D-mask effect show good model residual error, but fail to predict some critical CD tendencies. Rigorous simulation predicts the observed CD tendencies, which confirms that the discrepancy really comes from 3D-mask effect.

Is it possible to improve MEEF

As the design rule of device has shrunken, obtaining a feasible process window at low k1 factor in photolithography is the major concerning in order to shorten the total period from development to the mass production of devices. In this low k1 factor region, a tiny CD variation on mask might be increased abruptly on the wafer. In particular, such variation so called MEEF (Mask Error Enhancement Factor) is closely related with various types of process parameter. In this paper, we reviewed optimized process condition to minimize MEEF and defined uDoF (Usable Depth of Focus) considering a correlation between MEEF and DoF (Depth of Focus).