| Autor: |
Lin Cheng, Hongliang Lu, Silu Yan, Chen Liu, Jiantao Qiao, Junjun Qi, Wei Cheng, Yimen Zhang, Yuming Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Micromachines, Vol 14, Iss 11, p 2023 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2072-666X |
| DOI: |
10.3390/mi14112023 |
| Popis: |
In this paper, an aging small-signal model for degradation prediction of microwave heterojunction bipolar transistor (HBT) S-parameters based on prior knowledge neural networks (PKNNs) is explored. A dual-extreme learning machine (D-ELM) structure with an adaptive genetic algorithm (AGA) optimization process is used to simulate the fresh S-parameters of InP HBT devices and the degradation of S-parameters after accelerated aging, respectively. In addition to the reliability parametric inputs of the original aging problem, the S-parameter degradation trend obtained from the aging small-signal equivalent circuit is used as additional information to inject into the D-ELM structure. Good agreement was achieved between measured and predicted results of the degradation of S-parameters within a frequency range of 0.1 to 40 GHz. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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