Improved validation and optimization of physics-based NTD compact modeling flows

Abstract

Due to the semiconductor industry’s ever increasing need for finer resolution and improved critical dimension (CD) control, negative tone development (NTD) photoresists (resists) have been adopted for several advanced applications in lithographic patterning. NTD resists enable brightfield imaging by using an organic solvent developer to penetrate and remove the unexposed regions of the resist [1]. For certain critical patterning layers, such as metal trenches and vias, NTD resists are able to provide better resist imaging quality compared to the previous positive tone development (PTD) resist process. However, there are several additional engineering difficulties which must be addressed for an NTD resist process. Specifically, NTD resists have low contrast organic solvent development and in an NTD process the material remaining on the wafer substrate is exposed resist which has been substantially transformed both chemically and mechanically. Therefore, the remaining exposed resist shows significantly more complex physical behavior than the remaining PTD resist and these behaviors require substantial improvement in an OPC (compact) model’s physical modeling accuracy in order to match wafer data and trends [2,3]. Additionally, these more complex resist behaviors place further requirements on the physical validation of OPC modeling inference. In this paper, we present results of our work to understand and improve the optimization and physical validation of physics-based NTD compact modeling flows by utilizing new methods for analysis and automation. We utilize a complete compact model flow containing physics-based resist model forms for chemically amplified resist (CAR) exposure, CAR reaction-diffusion, resist top-loss due to exposure combined with post-exposure bake (PEB), low contrast organic solvent development of resist, and mechanical deformation effects in multiple process steps. We present solid evidence that this physically-based flow has been validated for accuracy and predictability by comparing it to several experimental NTD datasets and to results of rigorous 3D lithography simulation models which were trained to fit other experimental NTD data. We additionally compared key physics-based model forms from the compact model to the more complex full time-based moving surface NTD models of the rigorous 3D simulation. We next analyzed the key physics-based compact model forms for sensitivity to input testpattern type, layout and mask dimension (e.g., linearity and MEEF), traditional dose-focus variations, as well as systematic and random noise in CD metrology. We present the results of this study and make recommendations for minimum testpattern and overall process space data to include in NTD compact model datasets. We also present flow benefits obtained from automating different validation tests including the usefulness of employing rigorous lithography simulation NTD results early in the compact modeling flow to improve overall model quality. [1] S-H. Lee, et all. Understanding dissolution behavior of 193-nm photoresists in organic solvent developers.