Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells

Abstract

Silicon quantum dot multilayer (Si-QDML) is a promising material for a light absorber of all silicon tandem solar cells due to tunable bandgap energy in a wide range depending on the silicon quantum dot (Si-QD) size, which is possible to overcome the Shockley–Queisser limit. Since solar cell performance is degenerated by carrier recombination through dangling bonds (DBs) in Si-QDML, hydrogen termination of DBs is crucial. Hydrogen plasma treatment (HPT) is one of the methods to introduce hydrogen into Si-QDML. However, HPT has a large number of process parameters. In this study, we employed Bayesian optimization (BO) for the efficient survey of HPT process parameters. Photosensitivity (PS) was adopted as the indicator to be maximized in BO. PS (σp/σd) was calculated as the ratio of photoconductivity (σp) and dark conductivity (σd) of Si-QDML, which allowed the evaluation of important electrical characteristics in solar cells easily without fabricating process-intensive devices. 40-period layers for Si-QDML were prepared by plasma-enhanced chemical vapor deposition method and post-annealing onto quartz substrates. Ten samples were prepared by HPT under random conditions as initial data for BO. By repeating calculations and experiments, the PS was successfully improved from 22.7 to 347.2 with a small number of experiments. In addition, Si-QD solar cells were fabricated with optimized HPT process parameters; open-circuit voltage (VOC) and fill factor (FF) values of 689 mV and 0.67, respectively, were achieved. These values are the highest for this type of device, which were achieved through an unprecedented attempt to combine HPT and BO. These results prove that BO is effective in accelerating the optimization of practical process parameters in a multidimensional parameter space, even for novel indicators such as PS.

High Passivation Performance of Cat-CVD i-a-Si:H Derived from Bayesian Optimization with Practical Constraints

“…Recently, Bayesian optimization (BO), a machine learning method for sequential optimization, has been widely used to find optimum experimental conditions. − The BO algorithm provides the next experimental condition based on the probabilistic model for the regression of the data. By alternately repeating the experiments and determining the next experimental condition using the BO algorithm, the appropriate condition for maximizing (or minimizing) the object function can be determined with a small number of experiments.…”

Bayesian Optimization with Experience for Fast Development of Monolithic Tandem Solar Cells: Simulation Case Study

“…In a limited budget of only 48 experiments, authors demonstrated a champion PCE of 11.4% and predicted 14 new conditions yielding >10% PCEs. [1] BO was also used in the optimization of silicon quantum dot solar cells, [30] organic solar cells. [31][32][33] Another area where BO can be extremely helpful is the development of tandem solar cells.…”

Effect of Plasma Gas Type on the Operation Characteristics of a Three-Phase Plasma Reactor with Gliding Arc Discharge

“…Oxygen can be used in processes requiring an oxidizing environment [42,43]. Hydrogen is often used in arc plasmotrons because of its ability to produce highly reactive plasma [44,45]. It is used in processes such as plasma cleaning, surface activation and chemical vapor deposition.…”