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- W4382808599 abstract "Traditional Analog systems are usually designed by hand. With the advent of the increased complexity of semiconductor devices and analog circuits pertaining to their design, their analysis will become increasingly complicated, tedious, and time-consuming. Research of machine learning methods in the Analog Very Large Scale Integration domain is pivotal due to this increasing complexity and has a lot of scope for exploration. This paper aims to apply machine learning to obtain the aspect ratio of transistors in one of the most common analog circuits, viz., a two-stage operational amplifier. The circuit comprises eight transistors and the aspect ratio of each of the transistors is predicted through machine learning models for the given specifications for the 180 nm technology. The traditional method for calculation of aspect ratio, given the specifications, is a very tedious and time-consuming method and the exploitation of robust machine learning models for design would make the process convenient, fast, and reliable. This paper discusses the custom dataset creation process for training and testing the machine learning models, through the LTspice software. Further, the paper compares different machine learning models on the custom dataset through various standard evaluation metrics and visualizations to obtain the best-performing and the most reliable model for the aspect ratio estimation task. The comparison results showed that the neural network model was the best-performing model. Dropout was further incorporated into the model to reduce the extent of overfitting and improve performance. The proposed neural network model had a coefficient of determination of 0.936." @default.
- W4382808599 created "2023-07-02" @default.
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- W4382808599 date "2023-01-01" @default.
- W4382808599 modified "2023-09-26" @default.
- W4382808599 title "Aspect Ratio Estimation of a Two-Stage Operational Amplifier" @default.
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- W4382808599 doi "https://doi.org/10.1007/978-981-99-0973-5_40" @default.
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