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CCD and CMOS Imaging Sensors

CCD and CMOS Imaging Sensors is a research topic within Electrical and Electronic Engineering. Science Explorer counts 29k research works in it since 1950. 14.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the advancements in CMOS image sensor technology, including high-speed imaging, low-noise sensors, photon counting strategies, dynamic range enhancement, radiation effects, pixel-level ADC integration, temporal noise analysis, logarithmic response sensors, and their applications in biomedical imaging.

  • CMOS Image Sensors
  • High-Speed Imaging
  • Low-Noise Sensors
  • Photon Counting
  • Dynamic Range
  • Radiation Effects
  • Pixel-Level ADC
  • Temporal Noise Analysis
  • Logarithmic Response
  • Biomedical Imaging
Research works
29k
fractional, since 1950
In the world top 10%
4.1k
per year above
Top-10% rate
14.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+12%
the tick is no change

Which countries lead CCD and CMOS Imaging Sensors research?

By volume, China and the United States publish the most (1.7k and 760 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.7k works
  2. 2 United States 760 works
  3. 3 India 424 works
  4. 4 South Korea 324 works
  5. 5 Germany 205 works
  6. 6 Italy 200 works
  7. 7 Japan 195 works
  8. 8 United Kingdom 162 works
  9. 9 France 153 works
  10. 10 Taiwan 131 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 40.7%United States: 17.7%India: 9.9%South Korea: 7.5%6 others listed: 24.3%41%largest
China1,749 · 40.7%United States760 · 17.7%India424 · 9.9%South Korea324 · 7.5%6 others listed1,046 · 24.3%

Shares of the rows listed above, not of the whole node.

Which institutions lead CCD and CMOS Imaging Sensors research?

By volume in 2022–2025, Tsinghua University publishes the most CCD and CMOS Imaging Sensors research, followed by Chinese Academy of Sciences and Xidian University.

Who are the leading researchers in CCD and CMOS Imaging Sensors?

The most-cited researchers publishing on CCD and CMOS Imaging Sensors include Pietro Perona, M. Costa and M. Weber.

  1. 1 Pietro Perona United States 11k citations
  2. 2 M. Costa United Kingdom 9.7k citations
  3. 3 M. Weber France 9.7k citations
  4. 4 H. F-W. Sadrozinski United States 9.1k citations
  5. 5 M. Bóna United Kingdom 8.5k citations
  6. 6 G. Watts United States 8k citations
  7. 7 S. Grinstein United States 7.9k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is CCD and CMOS Imaging Sensors research done?

The largest centres of CCD and CMOS Imaging Sensors research in 2022–2025 are Beijing (China), Seoul (South Korea), Shanghai (China) and Xi'an (China). Among places with at least 20 works in it, it is an unusually large share of all research in Batavia and Hsinchu.

Largest cities, 2022–2025

  1. 1 Beijing China 451 works
  2. 2 Seoul South Korea 189 works
  3. 3 Shanghai China 167 works
  4. 4 Xi'an China 131 works
  5. 5 Nanjing China 96 works
  6. 6 Guangzhou China 78 works
  7. 7 Hangzhou China 76 works
  8. 8 Tokyo Japan 72 works
  9. 9 Chengdu China 71 works
  10. 10 Wuhan China 66 works

Where it is the local speciality

  1. BataviaUS · 22.1 works46×
  2. HsinchuTW · 57.1 works13×
← less than its size predictsmore →

Location quotient: how much more of its research is in CCD and CMOS Imaging Sensors than the world average.

See CCD and CMOS Imaging Sensors on the map

Where is the best place to study CCD and CMOS Imaging Sensors?

Among universities, judged by research, Xidian University, Tsinghua University and Peking University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%30%mean 17.75%fractional works in this node (log) →share in the world top 10% →Xidian University: 54, 12.2%Tsinghua University: 59, 18.2%Peking University: 53, 15.1%Seoul National University of Science and Technology: 10, 21.7%University of Science and Technology of China: 32, 14.9%Georgia Institute of Technology: 18, 24.2%University of Chinese Academy of Sciences: 41, 19.6%Sungkyunkwan University: 17, 19.6%Nanjing University of Posts and Telecommunications: 12, 19.6%National Yang Ming Chiao Tung University: 21, 12.4%Seoul National Unive…Tsinghua UniversityPeking UniversityXidian University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Xidian UniversityChina 68.612.2%14.1×54 +116.7%
2 Tsinghua UniversityChina 58.718.2%5.8×59 +16.2%
3 Peking UniversityChina 58.515.1%6.6×53 +39.8%
4 Seoul National University of Science and TechnologySouth Korea 58.221.7%13.5×10
5 University of Science and Technology of ChinaChina 57.814.9%5.2×32 +254.1%
6 Georgia Institute of TechnologyUnited States 55.924.2%5.7×18 +54.8%
7 University of Chinese Academy of SciencesChina 54.019.6%4.8×41 +23.4%
8 Sungkyunkwan UniversitySouth Korea 53.919.6%6.0×17 +96.1%
9 Nanjing University of Posts and TelecommunicationsChina 52.219.6%5.3×12 +192.2%
10 National Yang Ming Chiao Tung UniversityTaiwan 51.912.4%9.8×21 -22.7%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is CCD and CMOS Imaging Sensors research growing?

Output in 2018–2022 was 12% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are CCD and CMOS Imaging Sensors.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.