Process Window OPC Research Articles

Fast Lithographic Mask Optimization Considering Process Variation

As nanometer technology advances, conventional optical proximity correction (OPC) that minimizes the edge placement error (EPE) at the nominal process condition alone often leads to poor process windows. To improve the mask printability across various process corners, process-window OPC optimizes EPE for multiple process corners, but often suffers long runtime, due to repeated lithographic simulations. This paper presents an efficient process variation (PV)-aware mask optimization framework, namely PVOPC, to simultaneously minimize EPE and PV band with fast convergence. The PVOPC framework includes EPE-sensitivity-driven dynamic fragmentation, PV-aware EPE modeling, and correction with three new EPE-converging techniques and a systematic subresolution-assisted feature insertion algorithm. Experimental results show that our approach efficiently achieves high-quality EPE and PV band results.

Methods for joint optimization of mask and design targets for improving lithographic process window

Low-k 1 lithography results in features that suffer from poor lithographic yield in the presence of process variation. The problem is especially pronounced for lower-level metals used for local routing, where bi-directionality and tight pitches give rise to lithography unfriendly layout patterns. However, there exists inherent unutilized flexibility in design shapes, e.g., one can modify such wires without significantly affecting design behavior. We develop two different techniques to simultaneously modify mask and design shapes during optical proximity correction (OPC) to improve lithographic yield of low-level metal layers. The methods utilize image slope information, which is available during OPC image simulations at no extra cost, as a measure of lithographic process window. We first propose a method that identifies fragments with low normalized image log slope (NILS) and then use this NILS information to guide dynamic target modification between iterations of OPC. The method uses a pre-characterized lookup table to assign a different magnitude of local target correction to different NILS bins. Next we develop an optimization flow where we derive a cost function that maximizes both contour fidelity and robustness to drive our simultaneous mask and target optimization (SMATO) method. We develop analytical equations to predict the cost for a given mask and target modification and use a fast algorithm to minimize this cost function to obtain an optimal mask and target solution. Our experiments on sample 1× (M1) layouts show that the use of SMATO reduces the process manufacturability index (PMI) by 15.4% compared with OPC, which further leads to 69% reduction in the number of layout hotspots. Additionally, such improvement is obtained at low average runtime overhead (5.5%). Compared with process window optical proximity correction (PWOPC), we observe 4.6% improvement in PMI at large (2.6× ) improvement in runtime.

The Effects of Nominal Process Focus Definition on the OPC Model

Both nominal and process window (PW) OPC models were calibrated against wafer data from single and multiple process conditions, respectively. Based on different definition for nominal process focus condition, different OPC models were obtained. The effects of nominal process focus definition on the model performance were explored by analyzing the focus parameters calibrated and the data fitting accuracy and prediction capabilities of the models for different process conditions. For both nominal and PW OPC models, the nominal process focus setting has no obvious influence on fitting accuracy for particular process, however, proper nominal process focus selection results in better PW OPC model for nominal process defined.