Nowadays, the advanced usage of simulation tools for optical lithography requires substantial knowledge about the impact of model parameters and process conditions on simulation results. In many cases up to 30 or 40 parameters have to be tuned for different experimental data in order to obtain reliable simulation results. Consequently, the investigation of the impact of all model and process parameters on simulation results can be very time consuming. Therefore, we applied a correlation analysis, a well known statistical method, that allows a sensitivity analysis of simulation parameters. We compared the results of the sensitivity analysis method with the outcome of a standard “one-factor-at-a-time-method” and discuss the advantages and disadvantages of both methodologies. A calibrated ArF photoresist model has been examined with both sensitivity analysis methods.
The application of resolution enhancement techniques pushes optical projection lithography close to its theoretical limit with a k1-factor of 0.25. For the imaging close to this limit the interaction between the mask and the shape of the illumination aperture gains increasing importance. By jointly optimizing the mask and the source low k1 images can be printed with process latitudes not achievable otherwise. This paper proposes a new optimization procedure for mask and source geometries in optical projection lithography. A general merit function is introduced, that evaluates the imaging performance of specific patterns over a certain focus range. It also takes certain technological aspects, that are defined by the manufacturability and inspectability criteria for the mask, into account. Automatic optimization of the mask and illumination parameters with a genetic algorithm identifies optimum imaging conditions without any additional a-priori knowledge about lithographic processes. Several examples demonstrate the potential of the proposed concept.
This paper describes different simplified simulation models which characterize the behavior of the photoresist during lithography processes. The effectiveness of these models is compared with the results of more physics and chemistry containing simulators. The strengths and weaknesses of the simplified models are demonstrated for practical applications. Simplified resist model parameters are calibrated for 193nm chemically amplified resists (CAR). The results are compared with calibration of full simulation models. The validity of the simulation models under different process conditions is investigated.
This paper focuses on a novel methodology for a fast and efficient resist model calibration. One of the most crucial parts when calibrating a resist model is the fitting of experimental data where up to 20 resist model parameters are varied. Although general optimization approaches such as simplex algorithms or genetic algorithms have proven suitable for many applications, they do not exploit specific properties of resist models. Therefore, we have developed a new strategy based on Design of Experiment methods which makes use of these specific characteristics. This algorithm will be outlined and then be demonstrated by applying it to the calibration of a Solid-C resist model for one ArF line/space resist. As characterizing dataset we chose: a) a Focus Exposure Matrix (FEM) for the dense array, b) linearity, c) OPE (optical proximity) curve and e) the MEEF (mask error enhancement factor) for a dense array. It turned out that a simultaneous fit of the complete data set was not possible by varying resist parameters only. Considering the optical parameters appeared to be crucial as well. Therefore the influence of the optical settings (illumination, projection, 3D mask effects) on the lithography process will be discussed at this point. Finally we obtained an excellent matching of model predictions and experimental results.
This article proposes a new optimization procedure for mask and illumination geometries in optical projection lithography. A general merit function is introduced that evaluates the imaging performance of arbitrary line patterns over a certain focus range. It also takes into account certain technological aspects that are defined by the manufacturability and inspectability of the mask. Automatic optimization of the mask and illumination parameters with a genetic algorithm identifies optimum imaging conditions without any additional a-priori knowledge about lithographic processes. Several examples demonstrate the potential of the proposed concept.
Calibration of resist model parameters becomes more and more important in lithography simulation. The general goal of such a calibration procedure is to find parameters and model options which minimize the difference between experimentally measured and simulated data. In this paper a multidimensional downhill simplex method and a genetic algorithm are introduced. We investigate the performance of different modeling options such as the diffusivity of the photogenerated acid and of the quencher base, and different development models. Furthermore, new objective functions are proposed and evaluated: The overlap of process windows between simulated and experimental data and the comparison of linearity curves. The calibration procedures are performed for a 248nm and for a 193nm chemically amplified resist, respectively.
Post exposure bake (PEB) models in the lithography simulator SOLID-C have been extended in order to improve the description of kinetic and diffusion phenomena in chemically amplified resists. We have implemented several new models and options which take into account effects such as the diffusion of quencher base, different approaches to model the neutralization between photogenerated acid and a quencher base, spontaneous loss of quencher, and arbitrary dependencies of the diffusion coefficients on acid or inhibitor, respectively. In this study, the impact of these new model options on critical phenomena like iso-dense bias, linearity and line end shortening are examined. The simulations were performed for a calibrated KrF/ArF resist models.
In this paper we examine new models and the indispensability of model parameters of chemically amplified resists (CAR) for their usage in predictive process simulation. Based on a careful exploration of different modeling options we calibrate the model parameters with different experimental data. Furthermore, we investigate different modeling approaches: (1) Mode of coupling between diffusion and kinetic reactions, sequence of quencher base events (Hinsberg model); (2) Mode of diffusion: Fickian and linear diffusion model; (3) Development rate model: Performance of the Enhanced Notch model. The resulting models are evaluated with respect to their performance by comparing with experimental line-width for semidense (1-2, 1-1.6, 1-1.4, 1-1.2) and dense features, the bias between different features and full resist profiles. The investigations are applied to the Shipley resist UVTM 113. Finally, a parameter extraction procedure for chemically amplified resists is proposed.
Lithography simulators have become a standard tool in industrial and governmental research and development departments. IN contrast to the modeling approaches for the optical system and for the lithographic performance of i- line resists, there is still no consensus on the modeling of chemically amplified resist (CAR). Existing models differ in the description of the kinetics and the diffusion phenomena during post exposure bake and in the specification of the development rate. A modeling approach was established, that combines the light induced generation of photoacid, in- and out-diffusion of acid or base components, a generalized deprotection kinetics, Fickian and non-Fickian diffusion of resist components and an arbitrary development rate model. Existing models such as the effective acid model and a standard deprotection model for CAR can be considered as special cases of the implemented model. To evaluate the importance of certain options of the model and of the model parameters we have evaluated the performance of the model by comparing simulated CD data and resists profiles with experimental data.
KEYWORDS: Data modeling, Diffusion, Lithography, Scanning electron microscopy, Calibration, Optimization (mathematics), Performance modeling, Critical dimension metrology, Computer simulations, Picture Archiving and Communication System
The simulation of photolithographic processes depends on accurate resist modeling parameters. In this paper we present an automated fitting procedure which can be applied to arbitrary combinations of experimental data and model parameters. The procedure is applied to a typical i-line process. The resulting models are evaluated with respect to their performance for the full set of experimental data. The correlation of model parameters with certain experimental data is discussed and an optimum automatic parameters extraction procedure for i-line resists is proposed. Finally, we evaluate the extracted parameters by comparing different simulated profiles with cross-section SEM pictures.
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