Although the research, development and manufacturing applications of lithography simulation described above give full benefits of modeling based on time, cost and capacity, the potential capacity of simulation is its ability as a learning tool.
Although the advantages of simulated lithography in research, development and manufacturing applications have been proved by time, cost and performance, the potential of simulation lies in its ability as a learning tool.
The correct application of modeling allows users to learn efficiently and effectively.
Using simulation correctly can make users more handy.
There are many reasons why this is true.
There are several reasons for this.
First of all, the speed of simulation is the same as
Experiments make feedback more timely.
First of all, compared with the experiment, the simulation feedback speed is more timely.
Because learning is a cycle (an idea, an experiment, a measurement, and then compared with the original idea), faster feedback allows more learning cycles.
Because learning is a cycle (an idea, an experiment, a measurement, compared with the original), faster feedback can win more learning cycles.
Because simulation is very cheap, there are fewer restrictions and more opportunities to explore ideas.
Moreover, the simulation cost is low, the restrictions are few, and there are many opportunities to try.
Moreover, as the research application shows us, there are fewer physical restrictions on "experiments"
Can be executed.
As the results of research and application tell us, there are fewer physical restrictions on what kind of experiments.
All these factors allow modeling to be used to gain an understanding of lithography.
The application of all these simulation technologies makes us have a better understanding of lithography.
Whether learning basic concepts or exploring subtle differences, the value of improving knowledge will not be exaggerated.
Whether learning basic theoretical principles or exploring subtle differences in materials, the value of improving knowledge cannot be overestimated.
In the next chapter, the application of lithography simulation in manufacturing will be discussed in more detail.
In the following section, more important details about the application of analog lithography in manufacturing will be found.
2. Application of lithography simulation in manufacturing industry
2. Application of analog lithography technology in manufacturing industry.
A. Thin film stack optimization
A. Thin film stack optimization
For various reasons, thin film stack optimization is the most commonly used method for lithography simulation in manufacturing environment.
Analog lithography is most commonly used to optimize the film stack in a manufacturing environment for the following reasons.
First, the film stack changes frequently in the manufacturing plant, and it is not controlled by lithography except for the bottom anti-reflective coating (BARC) and the resist.
Group.
First, in fab, the stack of thin films often changes. Unlike the bottom anti-reflective coating and resist, this film stack is not controlled by lithography.
Therefore, the lithography team must respond to the changes of these thin film stacks by adjusting the lithography process.
Therefore, the lithography cluster must adjust the lithography process according to the changes of these film stacks.
From the point of view of lithography, the most important characteristic of thin film stack is the reflectivity of the substrate.
From the point of view of lithography, the most important part of thin film stack is the reflectivity of photosensitive substrate.
Unfortunately, when coated with resist, there is no way to measure the reflectivity of the substrate (the reflectivity of the substrate in air is meaningless for this application).
Undesirably, if the photosensitive substrate is coated with a layer of corrosion inhibitor, it is meaningless to measure the reflectivity of the photosensitive substrate in the air. It is impossible to measure the reflectivity of the photosensitive substrate.
Therefore, all BARC optimization work needs to use simulation.
Therefore, all BARC optimization work needs the application of simulation technology.
Conversely, this simulation needs to accurately measure the optical parameters (thickness, n&; k)。
In addition, this simulation technique requires optical parameters (thickness, n,&; K) accurate measurement.