Parametric Learning Algorithm  Python for Integrated Circuits   An Online Book  

Python for Integrated Circuits http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= Parametric learning algorithms are a class of machine learning algorithms that make specific assumptions about the functional form of the underlying data distribution. These assumptions simplify the learning process by reducing the problem to finding a set of parameters that fit this predetermined model form. These algorithms assume that the data can be adequately represented by a fixed number of parameters, which are estimated from the training data. The primary characteristic of parametric learning algorithms is that the model complexity is not determined by the size of the training dataset but rather by the complexity of the chosen model architecture. Some key characteristics and examples of parametric learning algorithms are:
In contrast to parametric learning, nonparametric learning methods, such as knearest neighbors (KNN) and decision trees, do not assume a fixed functional form and can adapt their complexity based on the data itself. Nonparametric models can be more flexible but may require more data and can be computationally expensive for large datasets. The choice between parametric and nonparametric learning depends on the nature of the data and the problem at hand. Parametric models are suitable when the underlying data distribution can be reasonably well approximated by the chosen model architecture and assumptions. A common application of parametric learning algorithms in the semiconductor industry is in the area of yield prediction and optimization. Specifically, linear regression models are often used to predict the yield of semiconductor manufacturing processes. In semiconductor manufacturing, yield refers to the proportion of devices on a wafer that function properly after the manufacturing process. Predicting yield is crucial because it influences production costs and overall profitability. Manufacturers collect extensive data on various process parameters and environmental conditions that could affect the yield, such as temperature, humidity, equipment settings, and material purity. In this example, linear regression model for yield prediction is used:
This approach leverages the assumption that the relationship between the process parameters and the yield is linear, which simplifies the modeling and computation. However, if this assumption does not hold true (e.g., if there are nonlinear dependencies), the prediction accuracy might suffer, indicating a potential need for more complex, possibly nonparametric models. ============================================


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