Genetic Algorithm for More Efficient Multi-layer Thickness Optimization in Solar Cell

09/14/2019
by   Premkumar Vincent, et al.
6

We propose to use Genetic Algorithm (GA), inspired by Darwin's evolution theory, to optimize the search for the optimal thickness in organic solar cell's layers with regards to maximizing the short-circuit current density. The conventional method used in optimization simulations, such as for optimizing the optical spacer layers' thicknesses, is the parameter sweep. Our experiments show that the introduction of GA results in a significantly faster and accurate search method when compared to brute-force parameter sweep method in both single and multi-layer optimization.

READ FULL TEXT

page 9

page 10

page 11

research
09/14/2019

Employing Genetic Algorithm as an Efficient Alternative to Parameter Sweep Based Multi-Layer Thickness Optimization in Solar Cells

Conventional solar cells are predominately designed similar to a stacked...
research
07/21/2023

CycleIK: Neuro-inspired Inverse Kinematics

The paper introduces CycleIK, a neuro-robotic approach that wraps two no...
research
10/28/2019

Computational design of organic solar cell active layer through genetic algorithm

The active layer microstructure of organic solar cells is critical to ef...
research
09/15/2019

Global optimization of parameters in the reactive force field ReaxFF for SiOH

We have used unbiased global optimization to fit a reactive force field ...
research
08/19/2020

Data Driven Optimization of Inter-Frequency Mobility Parameters for Emerging Multi-band Networks

Densification and multi-band operation in 5G and beyond pose an unpreced...
research
05/04/2020

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Current LTE network is faced with a plethora of Configuration and Optimi...
research
07/05/2019

Genetic Network Architecture Search

We propose a method for learning the neural network architecture that ba...

Please sign up or login with your details

Forgot password? Click here to reset