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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Bollam, John | - |
| dc.date.accessioned | 2025-12-17T07:09:09Z | - |
| dc.date.available | 2025-12-17T07:09:09Z | - |
| dc.date.issued | 2024-06 | - |
| dc.identifier.uri | http://localhost:8081/jspui/handle/123456789/18499 | - |
| dc.guide | Sarkar, Biplab | en_US |
| dc.description.abstract | This project aims to develop a broadband low-noise amplifier by using special properties of gallium nitride (GaN) transistors, with a specific focus on small signal models for AlGaN/GaN devices. Our main goals are to design broadband low noise amplifier by taking general and modified small signal models of GaN device. The general SSC models are unable to capture high frequency behaviour why because these elements are constants with frequency and also here each branch has first order elements. So, we are taking accurate SSC model, these elements are not constants, these elements are changing with frequency and also each branch has second order elements. So, it can capture high frequency behaviour also. So, we're using a more accurate way called a "second-order model". This is important, especially for things like microwave applications and RF electronic circuits at high frequencies, where accuracy really matters. To show how good this new way is, we design broadband low noise amplifiers using both the old and new SSC models. We found that the new modified SSC model is better at Gain and input matching networks but, it's a bit worse at adding extra noise. Overall, for high frequency broadband LNAs, the results demonstrated modified models are more accurate than conventional models. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IIT, Roorkee | en_US |
| dc.title | BROADBAND AMPLIFIER DESIGN USING ACCURATE GAN HEMT SMALL SIGNAL MODEL | en_US |
| dc.type | Dissertations | en_US |
| Appears in Collections: | MASTERS' THESES (E & C) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 22534005_BOLLAM JOHN.pdf | 6.89 MB | Adobe PDF | View/Open |
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