Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
Representative InGaAsP multiple-quantum-well distributed-feedback laser diode used as the modeled system for lifetime prediction and accelerated-aging comparison.
10 properties
SimulatedInGaAsP MQW-DFB laser diodeStudied Material
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
Representative InGaAsP multiple-quantum-well distributed-feedback laser diode used as the modeled system for lifetime prediction and accelerated-aging comparison.
10 properties
SimulatedInGaAsP MQW-DFB laser diodeStudied Material
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
Representative InGaAsP multiple-quantum-well distributed-feedback laser diode used as the modeled system for lifetime prediction and accelerated-aging comparison.
10 properties
SimulatedInGaAsP MQW-DFB laser diodeStudied Material
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
Representative InGaAsP multiple-quantum-well distributed-feedback laser diode used as the modeled system for lifetime prediction and accelerated-aging comparison.
10 properties
SimulatedInGaAsP MQW-DFB laser diodeStudied Material
Artificial neural network trained with back-propagation on synthetically generated lifetime and electro-optical laser data; compared against random forest and gradient boosting machine baselines; conventional Arrhenius-based lifetime projection used for comparison.
conventional referenceaccelerated aging test and Arrhenius projection
synthetic data generationanalytical
training validation splitnot stated
Conditions
validation:{"quantities":["MTTF","median lifetime","prediction error"],"references":["Han and Park (2007)"],"techniques":["accelerated aging test"],"source_type":"literature_experimental_data"}
Artificial neural network trained with back-propagation on synthetically generated lifetime and electro-optical laser data; compared against random forest and gradient boosting machine baselines; conventional Arrhenius-based lifetime projection used for comparison.
conventional referenceaccelerated aging test and Arrhenius projection
synthetic data generationanalytical
training validation splitnot stated
Conditions
validation:{"quantities":["MTTF","median lifetime","prediction error"],"references":["Han and Park (2007)"],"techniques":["accelerated aging test"],"source_type":"literature_experimental_data"}
Artificial neural network trained with back-propagation on synthetically generated lifetime and electro-optical laser data; compared against random forest and gradient boosting machine baselines; conventional Arrhenius-based lifetime projection used for comparison.
conventional referenceaccelerated aging test and Arrhenius projection
synthetic data generationanalytical
training validation splitnot stated
Conditions
validation:{"quantities":["MTTF","median lifetime","prediction error"],"references":["Han and Park (2007)"],"techniques":["accelerated aging test"],"source_type":"literature_experimental_data"}
Artificial neural network trained with back-propagation on synthetically generated lifetime and electro-optical laser data; compared against random forest and gradient boosting machine baselines; conventional Arrhenius-based lifetime projection used for comparison.
conventional referenceaccelerated aging test and Arrhenius projection
synthetic data generationanalytical
training validation splitnot stated
Conditions
validation:{"quantities":["MTTF","median lifetime","prediction error"],"references":["Han and Park (2007)"],"techniques":["accelerated aging test"],"source_type":"literature_experimental_data"}