In this paper, negative SU-8 photoresist processed at low temperature is characterized in terms of delamination. Based on a $3^3$ factorial designed experiment, 27 samples are fabricated, and the degree of delamination is measured for each. In addition, nine samples are fabricated for the...
In this paper, negative SU-8 photoresist processed at low temperature is characterized in terms of delamination. Based on a $3^3$ factorial designed experiment, 27 samples are fabricated, and the degree of delamination is measured for each. In addition, nine samples are fabricated for the purpose of verification. Employing the. neural network modeling technique, a process model is established, and response surfaces are generated to investigate degree of delamination associated with three process parameters: post exposure bake (PEB) temperature, PEB time, and exposure energy. From the response surfaces generated, two significant parameters associated with delamination are identified, and their effects on delamination are analyzed. Higher PEB temperature at a fixed PEB time results in a greater degree of delamination. In addition, a higher dose of exposure energy lowers the temperature at which the delamination begins and also results in a larger degree of delamination. These results identify acceptable ranges of the three process variables to avoid delamination of SU-8 film, which in turn might lead to potential defects in MEMS device fabrication.
In this paper, negative SU-8 photoresist processed at low temperature is characterized in terms of delamination. Based on a $3^3$ factorial designed experiment, 27 samples are fabricated, and the degree of delamination is measured for each. In addition, nine samples are fabricated for the purpose of verification. Employing the. neural network modeling technique, a process model is established, and response surfaces are generated to investigate degree of delamination associated with three process parameters: post exposure bake (PEB) temperature, PEB time, and exposure energy. From the response surfaces generated, two significant parameters associated with delamination are identified, and their effects on delamination are analyzed. Higher PEB temperature at a fixed PEB time results in a greater degree of delamination. In addition, a higher dose of exposure energy lowers the temperature at which the delamination begins and also results in a larger degree of delamination. These results identify acceptable ranges of the three process variables to avoid delamination of SU-8 film, which in turn might lead to potential defects in MEMS device fabrication.
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제안 방법
Inputs to the networks were the three parameters of interest (exposure energy, post-bake temperature, and post-bake time), and the output of the networks was the degree of delamination. "Hidden" neurons (neurons in the middle layers) extract nonlinear features from the data, and several networks with different numbers of hidden neurons were constructed and tested. The average RMS error in training was 2.
Extended development time may increase the chance of delamination of the exposed area from the substrate, but insufficient time may negatively impact the lithographic resolution[8]. In this research, a development time that allowed decent lithographic resolution was consistently used in order to avoid any additional complexity in characterization. The process variables and their ranges appear in Table 1.
However, it is necessary to perform a more systematic characterization experiment to clarify the relationship between process parameters and identify suitable ranges for process variables to ensure the fabrication without delamination. Therefore, this paper investigates the variation of low- temperature SU-8 processing, with the ultimate goal of minimizing delamination, using response surfaces generated from neural network models.
이론/모형
The leaning algorithm used in this study is the error back-propagation (BP) algorithm. A typical back- propagation neural network structure is depicted in Fig.
후속연구
By trial and error, delamination was reduced. However, it is necessary to perform a more systematic characterization experiment to clarify the relationship between process parameters and identify suitable ranges for process variables to ensure the fabrication without delamination. Therefore, this paper investigates the variation of low- temperature SU-8 processing, with the ultimate goal of minimizing delamination, using response surfaces generated from neural network models.
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