From ffb8b8090a264c26b896f662b67ca21a5066184b Mon Sep 17 00:00:00 2001 From: Ergun Simsek <58180288+simsekergun@users.noreply.github.com> Date: Fri, 3 Mar 2023 12:16:51 -0500 Subject: [PATCH] Update README.md --- README.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 7e9ed17..ab9e0a4 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,16 @@ -# 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)