cgl
/
photodetectors
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Update README.md

main
Ergun Simsek 2 years ago
committed by GitHub
parent
2a610476b7
commit
ffb8b8090a
No known key found for this signature in database GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 12 additions and 12 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 24
      README.md

24
README.md
View File

@ -1,16 +1,16 @@
# photodetectors # photodetectors <br>
MUTC Photodetector Dataset MUTC Photodetector Dataset <br>
This dataset lists four performance metrics of 1755 MUTC photodetectors with 17 layers. This dataset lists four performance metrics of 1755 MUTC photodetectors with 17 layers. <br>
The semiconductor types of these layers are fixed and they can be found in https://doi.org/10.1364/OE.27.003717 The semiconductor types of these layers are fixed and they can be found in https://doi.org/10.1364/OE.27.003717 <br>
**Columns** **Columns**
phasenoise : true phasenoise of the photodetector (Unit: dBc/Hz) phasenoise : true phasenoise of the photodetector (Unit: dBc/Hz) <br>
phasenoise_15mA : hypothetical phasenoise of the photodetector if the output current was equal to 15 mA (Unit: dBc/Hz) phasenoise_15mA : hypothetical phasenoise of the photodetector if the output current was equal to 15 mA (Unit: dBc/Hz) <br>
current : average current of the photodetector (Unit: mA) current : average current of the photodetector (Unit: mA) <br>
IR_max : maximum value of the impulse response of the photodetector IR_max : maximum value of the impulse response of the photodetector <br>
decay_time: the time it takes for the impulse response to decay from its maximum value to 1% of that maximum value (Unit: ns) decay_time: the time it takes for the impulse response to decay from its maximum value to 1% of that maximum value (Unit: ns) <br>
t1 ... t17 : thickness each layer (Unit: nm) t1 ... t17 : thickness each layer (Unit: nm) <br>
d1 ... d17 : doping concentration of each layer (Unit: cm^-3) d1 ... d17 : doping concentration of each layer (Unit: cm^-3) <br>
**Forward Problem:** Predicting Performance Metric From Design Parameters **Forward Problem:** Predicting Performance Metric From Design Parameters <br>
The four machine learning algorithms, linear regression, k-nearest neighbor, random forest, and an artificial neural network, are used to predict performance metrics (phasenoise, current, IR_Max, and decay_time) from the design parameters (thicknesses and doping concentrations) The four machine learning algorithms, linear regression, k-nearest neighbor, random forest, and an artificial neural network, are used to predict performance metrics (phasenoise, current, IR_Max, and decay_time) from the design parameters (thicknesses and doping concentrations)

Write Preview
Loading…
Cancel
Save