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适应新版keras

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cgldimo 2 months ago
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  1. 970
      MUTC1750_Predict_Metrics.ipynb
  2. 351
      MUTC_phasenoise_predictions.csv
  3. 351
      MUTC_phasenoise_truth.csv
  4. 400
      MUTC_testing_loss.csv
  5. 400
      MUTC_training_loss.csv
  6. 34
      README.md
  7. 1
      photodetectors

970
MUTC1750_Predict_Metrics.ipynb
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MUTC_phasenoise_predictions.csv
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400
MUTC_testing_loss.csv
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MUTC_training_loss.csv
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README.md
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# photodetectors <br>
MUTC Photodetector Dataset <br>
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 <br>
**Columns** <br>
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) <br>
current : average current of the photodetector (Unit: mA) <br>
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) <br>
t1 ... t17 : thickness each layer (Unit: nm) <br>
d1 ... d17 : doping concentration of each layer (Unit: cm^-3) <br>
**Forward Problem:** <br>
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)
# photodetectors <br>
MUTC Photodetector Dataset <br>
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 <br>
**Columns** <br>
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) <br>
current : average current of the photodetector (Unit: mA) <br>
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) <br>
t1 ... t17 : thickness each layer (Unit: nm) <br>
d1 ... d17 : doping concentration of each layer (Unit: cm^-3) <br>
**Forward Problem:** <br>
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)

1
photodetectors

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