Matching Items (3)
Filtering by
- All Subjects: Electrical Engineering
Description
Silicon photonic technology continues to dominate the solar industry driven by steady improvement in device and module efficiencies. Currently, the world record conversion efficiency (~26.6%) for single junction silicon solar cell technologies is held by silicon heterojunction (SHJ) solar cells based on hydrogenated amorphous silicon (a-Si:H) and crystalline silicon (c-Si). These solar cells utilize the concept of carrier selective contacts to improve device efficiencies. A carrier selective contact is designed to optimize the collection of majority carriers while blocking the collection of minority carriers. In the case of SHJ cells, a thin intrinsic a-Si:H layer provides crucial passivation between doped a-Si:H and the c-Si absorber that is required to create a high efficiency cell. There has been much debate regarding the role of the intrinsic a-Si:H passivation layer on the transport of photogenerated carriers, and its role in optimizing device performance. In this work, a multiscale model is presented which utilizes different simulation methodologies to study interfacial transport across the intrinsic a-Si:H/c-Si heterointerface and through the a-Si:H passivation layer. In particular, an ensemble Monte Carlo simulator was developed to study high field behavior of photogenerated carriers at the intrinsic a-Si:H/c-Si heterointerface, a kinetic Monte Carlo program was used to study transport of photogenerated carriers across the intrinsic a-Si:H passivation layer, and a drift-diffusion model was developed to model the behavior in the quasi-neutral regions of the solar cell. This work reports de-coupled and self-consistent simulations to fully understand the role and effect of transport across the a-Si:H passivation layer in silicon heterojunction solar cells, and relates this to overall solar cell device performance.
ContributorsMuralidharan, Pradyumna (Author) / Goodnick, Stephen M (Thesis advisor) / Vasileska, Dragica (Thesis advisor) / Honsberg, Christiana (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2019
Description
Semiconductor devices often face reliability issues due to their operational con-
ditions causing performance degradation over time. One of the root causes of such
degradation is due to point defect dynamics and time dependent changes in their
chemical nature. Previously developed Unified Solver was successful in explaining
the copper (Cu) metastability issues in cadmium telluride (CdTe) solar cells. The
point defect formalism employed there could not be extended to chlorine or arsenic
due to numerical instabilities with the dopant chemical reactions. To overcome these
shortcomings, an advanced version of the Unified Solver called PVRD-FASP tool was
developed. This dissertation presents details about PVRD-FASP tool, the theoretical
framework for point defect chemical formalism, challenges faced with numerical al-
gorithms, improvements for the user interface, application and/or validation of the
tool with carefully chosen simulations, and open source availability of the tool for the
scientific community.
Treating point defects and charge carriers on an equal footing in the new formalism
allows to incorporate chemical reaction rate term as generation-recombination(G-R)
term in continuity equation. Due to the stiff differential equations involved, a reaction
solver based on forward Euler method with Newton step is proposed in this work.
The Jacobian required for Newton step is analytically calculated in an elegant way
improving speed, stability and accuracy of the tool. A novel non-linear correction
scheme is proposed and implemented to resolve charge conservation issue.
The proposed formalism is validated in 0-D with time evolution of free carriers
simulation and with doping limits of Cu in CdTe simulation. Excellent agreement of
light JV curves calculated with PVRD-FASP and Silvaco Atlas tool for a 1-D CdTe
solar cell validates reaction formalism and tool accuracy. A closer match with the Cu
SIMS profiles of Cu activated CdTe samples at four different anneal recipes to the
simulation results show practical applicability. A 1D simulation of full stack CdTe
device with Cu activation at 350C 3min anneal recipe and light JV curve simulation
demonstrates the tool capabilities in performing process and device simulations. CdTe
device simulation for understanding differences between traps and recombination
centers in grain boundaries demonstrate 2D capabilities.
ditions causing performance degradation over time. One of the root causes of such
degradation is due to point defect dynamics and time dependent changes in their
chemical nature. Previously developed Unified Solver was successful in explaining
the copper (Cu) metastability issues in cadmium telluride (CdTe) solar cells. The
point defect formalism employed there could not be extended to chlorine or arsenic
due to numerical instabilities with the dopant chemical reactions. To overcome these
shortcomings, an advanced version of the Unified Solver called PVRD-FASP tool was
developed. This dissertation presents details about PVRD-FASP tool, the theoretical
framework for point defect chemical formalism, challenges faced with numerical al-
gorithms, improvements for the user interface, application and/or validation of the
tool with carefully chosen simulations, and open source availability of the tool for the
scientific community.
Treating point defects and charge carriers on an equal footing in the new formalism
allows to incorporate chemical reaction rate term as generation-recombination(G-R)
term in continuity equation. Due to the stiff differential equations involved, a reaction
solver based on forward Euler method with Newton step is proposed in this work.
The Jacobian required for Newton step is analytically calculated in an elegant way
improving speed, stability and accuracy of the tool. A novel non-linear correction
scheme is proposed and implemented to resolve charge conservation issue.
The proposed formalism is validated in 0-D with time evolution of free carriers
simulation and with doping limits of Cu in CdTe simulation. Excellent agreement of
light JV curves calculated with PVRD-FASP and Silvaco Atlas tool for a 1-D CdTe
solar cell validates reaction formalism and tool accuracy. A closer match with the Cu
SIMS profiles of Cu activated CdTe samples at four different anneal recipes to the
simulation results show practical applicability. A 1D simulation of full stack CdTe
device with Cu activation at 350C 3min anneal recipe and light JV curve simulation
demonstrates the tool capabilities in performing process and device simulations. CdTe
device simulation for understanding differences between traps and recombination
centers in grain boundaries demonstrate 2D capabilities.
ContributorsShaik, Abdul Rawoof (Author) / Vasileska, Dragica (Thesis advisor) / Ringhofer, Christian (Committee member) / Sankin, Igor (Committee member) / Brinkman, Daniel (Committee member) / Goodnick, Stephen (Committee member) / Bertoni, Mariana (Committee member) / Arizona State University (Publisher)
Created2019
Forward and Backward Machine Learning for Modeling Copper Diffusion in Cadmium Telluride Solar Cells
Description
To optimize solar cell performance, it is necessary to properly design the doping profile in the absorber layer of the solar cell. For CdTe solar cells, Cu is used for providing p-type doping. Hence, having an estimator that, given the diffusion parameter set (time and Temperature) and the doping concentration at the junction, gives the junction depth of the absorber layer, is essential in the design process of CdTe solar cells (and other cell technologies). In this work it is called a forward (direct) estimation process. The backward (inverse) problem then is the one in which, given the junction depth and the desired concentration of Cu doping at the CdTe/CdS heterointerface, the estimator gives the time and/or the Temperature needed to achieve the desired doping profiles. This is called a backward (inverse) estimation process. Such estimators, both forward and backward, do not exist in the literature for solar cell technology. To train the Machine Learning (ML) estimator, it is necessary to first generate a large set of data that are obtained by using the PVRD-FASP Solver, which has been validated via comparison with experimental values. Note that this big dataset needs to be generated only once. Next, one uses Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) to extract the actual Cu doping profiles that result from the process of diffusion, annealing, and cool-down in the fabrication sequence of CdTe solar cells. Two deep learning neural network models are used: (1) Multilayer Perceptron Artificial Neural Network (MLPANN) model using a Keras Application Programmable Interface (API) with TensorFlow backend, and (2) Radial Basis Function Network (RBFN) model to predict the Cu doping profiles for different Temperatures and durations of the annealing process. Excellent agreement between the simulated results obtained with the PVRD-FASP Solver and the predicted values is obtained. It is important to mention here that it takes a significant amount of time to generate the Cu doping profiles given the initial conditions using the PVRD-FASP Solver, because solving the drift-diffusion-reaction model is mathematically a stiff problem and leads to numerical instabilities if the time steps are not small enough, which, in turn, affects the time needed for completion of one simulation run. The generation of the same with Machine Learning (ML) is almost instantaneous and can serve as an excellent simulation tool to guide future fabrication of optimal doping profiles in CdTe solar cells.
ContributorsSalman, Ghaith (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen M. (Thesis advisor) / Ringhofer, Christian (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2021