Automated intelligent design of modified uni-traveling carrier photodectors Article Swipe
Junjing Huang
,
Xiaofeng Duan
,
Kai Liu
,
Yongqing Huang
,
Xiaomin Ren
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/oe.521441
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/oe.521441
This paper introduces an automatic intelligent design method for the modified uni-traveling carrier photodetector (MUTC-PD). The conventional photodetector design process often relies on the numerical solution of complex nonlinear partial differential equations to simulate and optimize device performance, which is not only computationally intensive but also inefficient. To overcome this challenge, we apply the charge control principle to calculate the photodetector bandwidth, which improves the computational speed by a factor of approximately 1800 compared to the numerical solution of nonlinear partial differential equations. To further optimize the structure of the photodetector, we incorporate the Velocity Varying Climbing Particle Swarm Optimization (VVCPSO) algorithm. This is an improved algorithm based on the traditional particle swarm algorithm, which is able to quickly find the optimal solution in a complex parameter space. By applying the VVCPSO algorithm, we successfully fine-tuned the photodetector structure and obtained structural parameters with optimal performance. Our thorough verification process confirms that the proposed method is consistent with the results of ATLAS simulation software. Automated design has resulted in a high-performance MUTC-PD with a responsivity of 0.52A/W and a bandwidth of 60 GHz (@-3 V) at a mesa diameter of 16µm. Compared to the pre-optimized device, the bandwidth is increased to three times the original. By reducing the mesa diameter to 4µm, the bandwidth can be further increased to 82 GHz (@-3 V). The proposed method's calculation speed is fast enough, enabling extensive parameter studies to optimize device performance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/oe.521441
- OA Status
- gold
- Cited By
- 1
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396230847
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396230847Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/oe.521441Digital Object Identifier
- Title
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Automated intelligent design of modified uni-traveling carrier photodectorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-04-29Full publication date if available
- Authors
-
Junjing Huang, Xiaofeng Duan, Kai Liu, Yongqing Huang, Xiaomin RenList of authors in order
- Landing page
-
https://doi.org/10.1364/oe.521441Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1364/oe.521441Direct OA link when available
- Concepts
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Photodetector, Particle swarm optimization, Bandwidth (computing), Responsivity, Computer science, Nonlinear system, Differential evolution, Algorithm, Optics, Physics, Telecommunications, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.partial | 29, 80 |
| abstract_inverted_index.process | 19, 149 |
| abstract_inverted_index.quickly | 118 |
| abstract_inverted_index.results | 159 |
| abstract_inverted_index.studies | 234 |
| abstract_inverted_index.(VVCPSO) | 100 |
| abstract_inverted_index.Climbing | 96 |
| abstract_inverted_index.Compared | 191 |
| abstract_inverted_index.Particle | 97 |
| abstract_inverted_index.Velocity | 94 |
| abstract_inverted_index.applying | 129 |
| abstract_inverted_index.compared | 73 |
| abstract_inverted_index.confirms | 150 |
| abstract_inverted_index.diameter | 188, 209 |
| abstract_inverted_index.enabling | 231 |
| abstract_inverted_index.improved | 105 |
| abstract_inverted_index.improves | 63 |
| abstract_inverted_index.method's | 225 |
| abstract_inverted_index.modified | 10 |
| abstract_inverted_index.obtained | 140 |
| abstract_inverted_index.optimize | 35, 85, 236 |
| abstract_inverted_index.overcome | 48 |
| abstract_inverted_index.particle | 111 |
| abstract_inverted_index.proposed | 153, 224 |
| abstract_inverted_index.reducing | 206 |
| abstract_inverted_index.resulted | 167 |
| abstract_inverted_index.simulate | 33 |
| abstract_inverted_index.solution | 25, 77, 122 |
| abstract_inverted_index.thorough | 147 |
| abstract_inverted_index.Automated | 164 |
| abstract_inverted_index.algorithm | 106 |
| abstract_inverted_index.automatic | 4 |
| abstract_inverted_index.bandwidth | 179, 197, 213 |
| abstract_inverted_index.calculate | 58 |
| abstract_inverted_index.equations | 31 |
| abstract_inverted_index.extensive | 232 |
| abstract_inverted_index.increased | 199, 217 |
| abstract_inverted_index.intensive | 43 |
| abstract_inverted_index.nonlinear | 28, 79 |
| abstract_inverted_index.numerical | 24, 76 |
| abstract_inverted_index.original. | 204 |
| abstract_inverted_index.parameter | 126, 233 |
| abstract_inverted_index.principle | 56 |
| abstract_inverted_index.software. | 163 |
| abstract_inverted_index.structure | 87, 138 |
| abstract_inverted_index.(MUTC-PD). | 14 |
| abstract_inverted_index.algorithm, | 113, 132 |
| abstract_inverted_index.algorithm. | 101 |
| abstract_inverted_index.bandwidth, | 61 |
| abstract_inverted_index.challenge, | 50 |
| abstract_inverted_index.consistent | 156 |
| abstract_inverted_index.equations. | 82 |
| abstract_inverted_index.fine-tuned | 135 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.parameters | 142 |
| abstract_inverted_index.simulation | 162 |
| abstract_inverted_index.structural | 141 |
| abstract_inverted_index.calculation | 226 |
| abstract_inverted_index.incorporate | 92 |
| abstract_inverted_index.intelligent | 5 |
| abstract_inverted_index.traditional | 110 |
| abstract_inverted_index.Optimization | 99 |
| abstract_inverted_index.conventional | 16 |
| abstract_inverted_index.differential | 30, 81 |
| abstract_inverted_index.inefficient. | 46 |
| abstract_inverted_index.performance, | 37 |
| abstract_inverted_index.performance. | 145, 238 |
| abstract_inverted_index.responsivity | 174 |
| abstract_inverted_index.successfully | 134 |
| abstract_inverted_index.verification | 148 |
| abstract_inverted_index.approximately | 71 |
| abstract_inverted_index.computational | 65 |
| abstract_inverted_index.photodetector | 13, 17, 60, 137 |
| abstract_inverted_index.pre-optimized | 194 |
| abstract_inverted_index.uni-traveling | 11 |
| abstract_inverted_index.photodetector, | 90 |
| abstract_inverted_index.computationally | 42 |
| abstract_inverted_index.high-performance | 170 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.53818707 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |