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-# photodetectors
-MUTC Photodetector Dataset
-This dataset lists four performance metrics of 1755 MUTC photodetectors with 17 layers.
-The semiconductor types of these layers are fixed and they can be found in https://doi.org/10.1364/OE.27.003717
+# photodetectors
+MUTC Photodetector Dataset
+This dataset lists four performance metrics of 1755 MUTC photodetectors with 17 layers.
+The semiconductor types of these layers are fixed and they can be found in https://doi.org/10.1364/OE.27.003717
**Columns**
-phasenoise : true phasenoise of the photodetector (Unit: dBc/Hz)
-phasenoise_15mA : hypothetical phasenoise of the photodetector if the output current was equal to 15 mA (Unit: dBc/Hz)
-current : average current of the photodetector (Unit: mA)
-IR_max : maximum value of the impulse response of the photodetector
-decay_time: the time it takes for the impulse response to decay from its maximum value to 1% of that maximum value (Unit: ns)
-t1 ... t17 : thickness each layer (Unit: nm)
-d1 ... d17 : doping concentration of each layer (Unit: cm^-3)
+phasenoise : true phasenoise of the photodetector (Unit: dBc/Hz)
+phasenoise_15mA : hypothetical phasenoise of the photodetector if the output current was equal to 15 mA (Unit: dBc/Hz)
+current : average current of the photodetector (Unit: mA)
+IR_max : maximum value of the impulse response of the photodetector
+decay_time: the time it takes for the impulse response to decay from its maximum value to 1% of that maximum value (Unit: ns)
+t1 ... t17 : thickness each layer (Unit: nm)
+d1 ... d17 : doping concentration of each layer (Unit: cm^-3)
-**Forward Problem:** Predicting Performance Metric From Design Parameters
+**Forward Problem:** Predicting Performance Metric From Design Parameters
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)