Sedova V (2022)
Publication Language: English
Publication Type: Thesis
Publication year: 2022
Grayscale lithography uses established processes from semiconductor technology and, therefore,
provides the ideal starting point for wafer level optics and large-area structures. However,
the development of product specific processes for grayscale lithography is extremely
demanding, costly and time-consuming. This Master’s thesis aims to develop an accurate
and robust model with main emphasis on the thick photoresist effect due to the presence
of residual solvent inside the photoresist after spin coating and prebake. To fabricate a
certain target layout, different patterns should be simulated, then the model should be
calibrated to predict the experimental profile for a given dose and height of photoresist.
Once the model is calibrated with experimental data, it should predict the dose distribution
(and process conditions) to fabricate a certain target layout. The final goal is to set
up a neural network and to use this model to generate data, free form profiles, for deep
learning applications. This enables the realization of numerous innovative products using
grayscale lithography more flexible and efficient.
APA:
Sedova, V. (2022). Modeling of thick photoresist for grayscale lithography application (Master thesis).
MLA:
Sedova, Valeriia. Modeling of thick photoresist for grayscale lithography application. Master thesis, 2022.
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