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

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 README.md | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
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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	<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**
-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)	<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:** 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)