A Neural Network Approach to Transistor Circuit Design

Thomas L. Hemminger

A Neural Network Approach to Transistor Circuit Design

Thomas L. Hemminger * §

Contact

Penn State University

A Neural Network Approach to Transistor Circuit Design

Article Fingerprint

ReserarchID

CSTF57CS

A Neural Network Approach to Transistor Circuit Design Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

Abstract

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.

References

2 Cites in Article

Reference Format

A Sedra,K Smith (2015). Microelectronic Circuits.
R Jaeger (1997). M.D. Bryce Policies and Methods for Industrial Development. Mc Graw-Hill Series in International Development. New York, London, Sydney, Toronto, Mc Graw-Hill Book Company, 1965, X p. 309 p., 60/–..

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Thomas L. Hemminger. 2016. "A Neural Network Approach to Transistor Circuit Design". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 16 (GJCST Volume 16 Issue D1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 16 Issue D1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-D Classification C.2.1

Submission ReceivedDecember 7, 2015
Peer Review Double Blind
Handling Editor
Accepted December 31, 2015
Published January 15, 2016

Version of record

v1.2

Issue date

December 16, 2016

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 171

Country: United States

Subject: Global Journal of Computer Science and Technology

Authors: Thomas L. Hemminger (PhD/Dr. count: 0)

View Count (all-time): 505

Total Views (Real + Logic): 2849

Total Downloads (simulated): 178

Publish Date: 2016 01, Fri

Monthly Totals (Real + Logic):

Month 1: 33 views
Month 2: 67 views
Month 3: 48 views
Month 4: 29 views
Month 5: 33 views
Month 6: 45 views
Month 7: 16 views
Month 8: 19 views
Month 9: 37 views
Month 10: 30 views
Month 11: 31 views
Month 12: 47 views
Month 13: 17 views
Month 14: 42 views
Month 15: 38 views
Month 16: 38 views
Month 17: 34 views
Month 18: 35 views
Month 19: 32 views
Month 20: 44 views
Month 21: 23 views
Month 22: 38 views
Month 23: 38 views
Month 24: 38 views
Month 25: 47 views
Month 26: 27 views
Month 27: 36 views
Month 28: 30 views
Month 29: 34 views
Month 30: 38 views
Month 31: 25 views
Month 32: 12 views
Month 33: 36 views
Month 34: 27 views
Month 35: 42 views
Month 36: 35 views
Month 37: 21 views
Month 38: 20 views
Month 39: 42 views
Month 40: 32 views
Month 41: 50 views
Month 42: 38 views
Month 43: 36 views
Month 44: 16 views
Month 45: 38 views
Month 46: 47 views
Month 47: 14 views
Month 48: 29 views
Month 49: 24 views
Month 50: 42 views
Month 51: 16 views
Month 52: 29 views
Month 53: 32 views
Month 54: 40 views
Month 55: 17 views
Month 56: 26 views
Month 57: 30 views
Month 58: 27 views
Month 59: 21 views
Month 60: 42 views
Month 61: 32 views
Month 62: 17 views
Month 63: 24 views
Month 64: 18 views
Month 65: 27 views
Month 66: 39 views
Month 67: 35 views
Month 68: 34 views
Month 69: 22 views
Month 70: 33 views
Month 71: 47 views
Month 72: 29 views
Month 73: 34 views
Month 74: 45 views
Month 75: 16 views
Month 76: 40 views
Month 77: 44 views
Month 78: 23 views
Month 79: 11 views
Month 80: 33 views
Month 81: 35 views
Month 82: 37 views
Month 83: 30 views
Month 84: 31 views
Month 85: 29 views
Month 86: 49 views
Month 87: 29 views
Month 88: 40 views
Month 89: 31 views
Month 90: 21 views
Month 91: 18 views
Month 92: 37 views
Month 93: 31 views
Month 94: 17 views
Month 95: 23 views
Month 96: 41 views
Month 97: 35 views
Month 98: 38 views
Month 99: 35 views
Month 100: 28 views
Month 101: 35 views
Month 102: 38 views
Month 103: 14 views
Month 104: 28 views
Month 105: 33 views
Month 106: 30 views
Month 107: 21 views
Month 108: 15 views
Month 109: 17 views
Month 110: 31 views
Month 111: 54 views
Month 112: 54 views
Month 113: 42 views
Month 114: 57 views
Month 115: 30 views
Month 116: 29 views
Month 117: 37 views
Month 118: 30 views
Month 119: 38 views
Month 120: 14 views
Month 121: 47 views
Month 122: 32 views
Month 123: 24 views
Month 124: 42 views
Month 125: 46 views
Month 126: 55 views
Month 127: 58 views
Month 128: 42 views
Month 129: 42 views

Total Views: 2.8K

Total Downloads: 178

All Trends