This paper studies the application of resist models to AIMSTM images. Measured AIMSTM data were coupled with
resist simulations of the Fraunhofer IISB research and development lithography simulator Dr.LiTHO and with a
compact resist model developed by Carl Zeiss SMS. Through-focus image data of the AIMSTM are transformed into a
bulk image--the intensity distribution within the resist. This bulk image is used to compute the concentration of photo-acid
after exposure and the following resist processing. In the result a resist profile is obtained, which can be used to
extract the printed wafer linewidth and other data. Additionally, a compact resist model developed by Carl Zeiss SMS
was directly applied to the AIMSTM data. The described procedures are used to determine dose latitudes for lines and
spaces with different pitches. The obtained data are compared to actual wafer prints for a 1.2 NA system.
The introduction of double patterning and double exposure technologies, especially in combination with hyper NA,
increases the importance of wafer topography phenomena. Rigorous electromagnetic field (EMF) simulations of two
beam interference exposures over non-planar wafers are used to explore the impact of the hardmask material and pattern
on resulting linewidths and swing curves after the second lithography step. Moreover, the impact of the optical material
contrast between the frozen and unfrozen resist in a pattern freezing process and the effect of a reversible contrast enhancement
layer on the superposition of two subsequent lithographic exposures are simulated. The described simulation
approaches can be used for the optimization of wafer stack configurations for double patterning and to identify appropriate
optical material properties for alternative double patterning and double exposure techniques.
KEYWORDS: Line edge roughness, Polymers, Diffusion, Molecules, Materials processing, Photoresist processing, Stochastic processes, Monte Carlo methods, Image quality, Projection systems
The reduction of semiconductor device dimensions necessitates, amongst others, a reduction of the line-edge
roughness (LER) of the lithographically patterned device components. Experimentally, the impact of many
process and material parameters on resist LER has been demonstrated. The impact of some parameters on
LER has also been described quantitatively. This paper presents a mesoscopic (i.e., discrete and stochastic)
modeling approach including all exposure, post-exposure bake (PEB), and development related parameters and
their impact on LER. This allows a prediction of the resulting resist profiles including average dimensions as well
as LER. The mesoscopic models are applied for simulating the impact of aerial image contrast, acid diffusion
length, and quencher base concentration on LER. The results are compared to experimental data. After this
validation of the models, they are applied for LER optimization. The optimum combination of acid and base
diffusion length is identified for resist formulations with various levels of base concentration. While the impact of
acid diffusion length is already known in principle, it is shown in this paper for the first time how the optimum
acid diffusion length depends crucially on base di®usion length and initial base concentration of the resist.
This paper introduces Dr.LiTHO, a research and development oriented lithography simulation environment developed
at Fraunhofer IISB to flexibly integrate our simulation models into one coherent platform. We propose a light-weight
approach to a lithography simulation environment: The use of a scripting (batch) language as an integration platform.
Out of the great variety of different scripting languages, Python proved superior in many ways: It exhibits a good-natured
learning-curve, it is efficient, available on virtually any platform, and provides sophisticated integration mechanisms for
existing programs. In this paper, we will describe the steps, required to provide Python bindings for existing programs
and to finally generate an integrated simulation environment. In addition, we will give a short introduction into selected
software design demands associated with the development of such a framework. We will especially focus on testing and
(both technical and user-oriented) documentation issues.
Dr.LiTHO Python files contain not only all simulation parameter settings but also the simulation flow, providing maximum
flexibility. In addition to relatively simple batch jobs, repetitive tasks can be pooled in libraries. And as Python
is a full-blown programming language, users can add virtually any functionality, which is especially useful in the scope
of simulation studies or optimization tasks, that often require masses of evaluations. Furthermore, we will give a short
overview of the numerous existing Python packages. Several examples demonstrate the feasibility and productiveness of
integrating Python packages into custom Dr.LiTHO scripts.
This paper illustrates the use of genetic algorithms (GA) in optimizing mask and illumination source geometries for lithographic imaging systems. The main goal of the proposed optimization process is to find optimum conditions for the generation of certain features like lines and spaces patterns or arrays of contact holes by optical projection lithography. Therefore, different optical resolution enhancement techniques, such as optical proximity correction (OPC) by sub-resolution assists, phase shift masks, and off-axis illumination techniques are combined and mutually optimized. This paper focuses on improving both the genetic algorithm's settings and the representation of the mask and source geometries. It is shown that these two issues have a significant impact on the convergence behavior of the GA. Different representation types for the mask and source geometry are introduced, and their advantages and problems are discussed. One of the most critical tasks in formulating the optimization problem is to set up an appropriate fitness function. In our case, the fitness function consists of five sub-functions, which ensure valid geometries, correct feature dimensions, a stable process for different defocus settings, the mask's manufacturability and inspectability, and that no other features besides the specified target are printed. In order to obtain a stable and fast convergence these criteria have to be assessed. Different weight settings are introduced and their impact on the convergence behavior is discussed.
Several results show the potential of the proposed approach and directions for further improvements.
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.
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