In this thesis, we present an efficient implementation of the generalized Time Transfer Constant algorithm. It is used to determine the symbolic transfer function of a linearized circuit with a desired level of accuracy, thus accepting its tradeoff for a reduced number of calculations. The algorithm is implemented in Python and has been tested on a variety of linear circuits, and the results tradeoffs are discussed. The algorithm has the potential to be used in a variety of applications, mainly EDA and critical element isolation.

In this thesis, we present an efficient implementation of the generalized Time Transfer Constant algorithm. It is used to determine the symbolic transfer function of a linearized circuit with a desired level of accuracy, thus accepting its tradeoff for a reduced number of calculations. The algorithm is implemented in Python and has been tested on a variety of linear circuits, and the results tradeoffs are discussed. The algorithm has the potential to be used in a variety of applications, mainly EDA and critical element isolation.

### Efficient algorithm based on the TTC method for the symbolic analysis of the transfer function

#### Abstract

In this thesis, we present an efficient implementation of the generalized Time Transfer Constant algorithm. It is used to determine the symbolic transfer function of a linearized circuit with a desired level of accuracy, thus accepting its tradeoff for a reduced number of calculations. The algorithm is implemented in Python and has been tested on a variety of linear circuits, and the results tradeoffs are discussed. The algorithm has the potential to be used in a variety of applications, mainly EDA and critical element isolation.
##### Scheda Scheda DC
2022
Efficient algorithm based on the TTC method for the symbolic analysis of the transfer function
In this thesis, we present an efficient implementation of the generalized Time Transfer Constant algorithm. It is used to determine the symbolic transfer function of a linearized circuit with a desired level of accuracy, thus accepting its tradeoff for a reduced number of calculations. The algorithm is implemented in Python and has been tested on a variety of linear circuits, and the results tradeoffs are discussed. The algorithm has the potential to be used in a variety of applications, mainly EDA and critical element isolation.
Symbolic analysis
Analog electronics
Jupyter notebook
Python
EDA
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/20.500.12608/53302`