A Neural Network Approach to Transistor Circuit Design
Transistor amplifier design is an important and fundamental concept in electronics, typically encountered by students at the junior level in electrical engineering. This paper focuses on two configurations that employ neural networks to design bipolar junction transistor circuits. The purpose of this work is to determine which design best fits the required parameters. Engineers often need to develop transistor circuits using a particular topology, e.g., common emitter, common collector, or common base. These also include a set of parameters including voltage gain, input impedance, and output impedance. For the most part, there are several methodologies that can provide a suitable solution, however the objective of this work is to indicate which external resistors are necessary to yield useful designs by employing neural networks. Here, a neural network has been trained to supply these component values for a particular configuration based on the aforementioned parameters. This should save a significant amount of work when evaluating a particular topology. And it should also permit experimentation with several designs, without having to perform detailed calculations.
