True process variation aware optical proximity correction with variational lithography modeling and model calibration

Abstract

Optical proximity correction (OPC) is one of the most widely used Resolution Enhancement Techniques (RET) in nanometer designs to improve subwavelength printability. Conventional model-based OPC assumes nominal process conditions without considering process variations because of the lack of variational lithography models. A simple method to improve OPC results under process variations is to sample multiple process conditions across the process window, which requires long runtime. We derive a variational lithography model (VLIM) which can simulate across the process window without much runtime overhead compared to the conventional lithography models. To match the model to experimental data, we demonstrate VLIM calibration method. The calibrated model has accuracy comparable to non-variational models, but it has the advantage of taking process variations into consideration. We introduce the variational edge placement error (V-EPE) metrics based on the model, a natural extension to the edge placement error (EPE) used in conventional OPC algorithms. A true process-variation aware OPC (PV-OPC) framework is proposed using the V-EPE metric. Due to the analytical nature of VLIM, our PV-OPC is only about 2-3× slower than the conventional OPC, but it explicitly considers the two main sources of process variations (exposure dose and focus variations) during OPC. Thus our post PV-OPC results are much more robust than the conventional OPC ones, in terms of both geometric printability and electrical characterization under process variations.

Improving the Quality of Delay-Based PUFs via Optical Proximity Correction

“…Dose variations are often modeled by replacing the constant I th in (5) with a random variable [18].…”

Impact of photomask shape uncertainties on computational lithography

“…However, the accuracy is usually obstructed by the uncertainties that exist broadly in the system [1]. The popular uncertainty concern includes the dose and defocuses, making the size of process window a main metric for evaluating lithographic performance [2], [3]. Another major source of uncertainty is the mask shape variation, which can be caused by a variety of reasons, such as the lens heating behavior [4], mask manufacturing error and mask representation error [5].…”

Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

“…6,7 However, most published optimization technologies were implemented under fixed process conditions. 3,8,9 Moreover, lithography-tool parameters such as the numerical aperture (NA) and source parameter also determine the PW. 1 Actually, the parameters related to the mask, process, and lithography tool simultaneously impact the lithography performance.…”