scholarly journals Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing

2021 ◽  
Author(s):  
Zhe Liu ◽  
Nicholas Rolston ◽  
Austin Cristobal Flick ◽  
Thomas W. Colburn ◽  
Zekun Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Process Optimization ◽  
Perovskite Solar Cell ◽  
Cell Manufacturing

Application of Machine Learning in Perovskite Solar Cell Crystal Size Distribution Analysis

MRS Advances ◽  
2019 ◽  
Vol 4 (14) ◽  
pp. 793-800 ◽  
Author(s):  
Thomas Chen ◽  
Yuchen Zhou ◽  
Miriam Rafailovich
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Edge Detection ◽  
True Positive Rate ◽  
Perovskite Solar Cell ◽  
Higher Order ◽  
True Positive ◽  
Degree Polynomial ◽  
Positive Rate ◽  
Crystal Grains

AbstractThis research automates edge detection for perovskite crystal grains using machine learning (ML). Once the edges of the crystal grains are located, a flood-fill algorithm can be used to find the distribution of crystal grain areas. The ML algorithm uses GNU Octave to run a regularized logistic regression algorithm that classifies each pixel of an input image as part of an edge or not part of an edge. The basic features used for the algorithm are each pixel’s grayscale intensity, its Sobel derivative. Higher order Sobel derivatives, higher degree polynomial terms, and intensities convolved by various kernels were used as additional features to improve the program’s accuracy and true-positive rate. Training data is obtained by using non-ML Canny Edge Detection to annotate the edges an SEM image of a pure perovskite solar cell (PSC). The classifier exhibits an 85.58% accuracy and produces an edge mask that clearly outlines the crystals visually. The ML edge mask exhibits far fewer false-positive mis-classifications for pixels in the middle of the crystals than Canny. However, the ML mask’s edges are fainter, owing to a lower true-positive classification rate. Using more kernels, higher order derivatives, and higher degree polynomial terms all significantly increased the true positive rate of the classifier, leading to thicker edges. This algorithm can greatly accelerate perovskite solar cell research (and potentially any research requiring particle size analysis), automating a process scientists previously had to perform by hand. This will facilitate the search for a solution for the world’s growing demands for renewable energy.

Up-scaling perovskite solar cell manufacturing from sheet-to-sheet to roll-to-roll: challenges and solutions

2017 ◽  
Author(s):  
Francesco Di Giacomo ◽  
Yulia Galagan ◽  
Santhosh Shanmugam ◽  
Harrie Gorter ◽  
Fieke van den Bruele ◽  
...  
Keyword(s):  
Solar Cell ◽  
Perovskite Solar Cell ◽  
Roll To Roll ◽  
Cell Manufacturing ◽  
Up Scaling

Process optimization for improving drilling efficiency in EWT solar cell manufacturing

2010 ◽  
Author(s):  
Henrikki Pantsar ◽  
Thomas Eisenbeis ◽  
Mathew Rekow ◽  
Richard Murison ◽  
Hans Herfurth ◽  
...  
Keyword(s):  
Solar Cell ◽  
Process Optimization ◽  
Cell Manufacturing

Consistent Device Model of a Perovskite Solar Cell for Multiple Experiments

2019 ◽  
Author(s):  
Evelyne Knapp ◽  
Andreas Schiller ◽  
Martin T. Neukom ◽  
Simon Züfle ◽  
Beat Ruhstaller
Keyword(s):  
Solar Cell ◽  
Perovskite Solar Cell ◽  
Device Model
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