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**
**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>
@ -12,5 +12,6 @@ decay_time: the time it takes for the impulse response to decay from its maximum
t1 ... t17 : thickness each layer (Unit: nm) <br>
d1 ... d17 : doping concentration of each layer (Unit: cm^-3) <br>
**Forward Problem:** Predicting Performance Metric From Design Parameters <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)