JOURNAL ARTICLE
Programmable Optically Variable Resistors: Automating the Design and Measurement of Transistor Biasing Circuits.
Published In: Journal of Circuits, Systems & Computers, 2025, v. 34, n. 10. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ray, S.; Acharyya, A.; Sarkar, A.; Das, P.; Das, R.; Halder, T.; Maji, A.; Chowdhury, S. B. R.; Hossain, S. R.; Gain, A.; Mondal, S. S.; Mondal, S.; Adhikari, S. K. 3 of 3
Abstract
This paper presents a solution for implementing basic electronic circuits related to the limited availability of precise resistor values. Traditional fixed-value resistors often cause deviations between desired and actual outputs. To address this, a programmable optically variable resistor (POVR) device is introduced, offering a wide range of resistance settings by adjusting input voltage. The POVR uses an optically coupled light-emitting diode (LED) and light-dependent resistor (LDR) pair to modulate resistance by varying LED light intensity, minimizing output deviations in circuit design. Additionally, the authors develop a POVR-enabled transistor bias automation system (POVR-ETBAS) for automatic resistance measurement and circuit optimization. This system provides automated circuit design and measurement functionalities. Demonstrations on common emitter (CE) mode BJT self-bias circuits show promising results in achieving desired resistance values and accurate measurements. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2025/07, Vol. 34, Issue 10, p1
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2025
- ISSN:0218-1266
- DOI:10.1142/S0218126625502196
- Accession Number:185744499
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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