Framework to predict optimal buffer layer pairing for thin film solar cell absorbers: A case study for tin sulfide/zinc oxysulfide

Mangan, Niall M.; Brandt, Riley E.; Steinmann, Vera; Jaramillo, Rafael; Yang, Chuanxi; Poindexter, Jeremy R.; Chakraborty, Rupak; Park, Helen Hejin; Zhao, Xizhu; Gordon, Roy G.; Buonassisi, Tonio

doi:10.1063/1.4930581

Cited by 29 publications

(23 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of this analysis are shown in Figure 3. The conduction band offset was fit to be -0.21 ± 0.03 eV, consistent with the previous measurement of -0.38 ± 0.2 eV [7] and fit with a higher precision than direct measurement allows.…”

Section: Jvti Measurements On Sns Solar Cellssupporting

confidence: 84%

Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods

Kurchin¹,

Poindexter²,

Kitchaev³

et al. 2018

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC &Amp

Self Cite

View full text Add to dashboard Cite

Defects in semiconductors, although atomistic in scale and often scarce in concentration, frequently represent the performance-limiting factor in optoelectronic devices such as solar cells. However, due to this scale and scarcity, direct experimental characterization of defects is technically challenging, timeconsuming, and expensive. Even so, the fact that defects can limit device performance suggests that device-level characterization should be able to lend insight into their properties. In this work, we use Bayesian inference to demonstrate a way to relate experimental device measurements with defect properties (as well as other materials properties affected by the presence of defects, such as minority-carrier lifetime). We apply this method to solve the "inverse problem" to a forward device model -namely, determining which input parameters to the model produce the measured electrical output. This approach has distinct advantages over direct characterization. First, a single set of measurements can be used to determine many parameters (the number of which, in principle, is limited only by the computing resources available), saving time and cost of facilities and equipment. Second, since measurements are performed on materials and interfaces in their relevant device geometries (vs. separately prepared samples), the determined parameters are guaranteed to be physically relevant. We demonstrate application of this method to both tin monosulfide and silicon solar cells and discuss potential for future application in a broader array of systems.

show abstract

Section: Jvti Measurements On Sns Solar Cellssupporting

confidence: 84%

Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods

Kurchin¹,

Poindexter²,

Kitchaev³

et al. 2018

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC &Amp

Self Cite

View full text Add to dashboard Cite

show abstract

“…SnS has a direct band gap of 1.3 eV and an indirect band gap of 1.1 eV, which are nearly ideal for solar cell absorbers. [3][4][5] It has native p-type conduction due to the small enthalpy of formation of tin vacancies, which generate shallow acceptors. 4,6 SnS has a high absorption coefficient (a > 10 4 cm À1 ), so SnS layers less than 500 nm thick can absorb 75% of the solar spectrum above the direct band gap.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies show that this thin oxide layer can passivate the SnS/buffer layer interface, which leads to improved solar cell efficiency. 1,2,5,7 To minimize the series resistance and improve the performance of SnS solar cells, it is important to understand the electrical properties of the interface between SnS and contact metals. SnS is a p-type semiconductor with a high ionization potential.…”

Section: Introductionmentioning

confidence: 99%

Measurement of contact resistivity at metal-tin sulfide (SnS) interfaces

Yang

Sun

Brandt

et al. 2017

Journal of Applied Physics

Self Cite

View full text Add to dashboard Cite

We measured the contact resistivity between tin(II) sulfide (SnS) thin films and three different metals (Au, Mo, and Ti) using a transmission line method (TLM). The contact resistance increases in the order Au < Mo < Ti. The contact resistances for Au and Mo are low enough so that they do not significantly decrease the efficiency of solar cells based on SnS as an absorber. On the other hand, the contact resistance of Ti to SnS is sufficiently high that it would decrease the efficiency of a SnS solar cell using Ti as a back contact metal. We further estimate the barrier heights of the junctions between these metals and tin sulfide using temperature-dependent TLM measurements. The barrier heights of these three metals lie in a narrow range of 0.23-0.26 eV, despite their large differences in work function. This Fermi level pinning effect is consistent with the large dielectric constant of SnS, and comparable to Fermi-level pinning on Si. The contact resistivity between annealed SnS films and Mo substrates under light illumination is as low as 0.1 X cm 2 .

show abstract

“…This interplay is illustrated by a typical early-stage PV material, tin monosulfide (SnS). Over a decade of research has led to PV conversion efficiencies in excess of 4%, 14 due to advances in SnS fabrication techniques, 15,16 control of SnS doping and transport properties, 17,18 control of interfaces, 19,20 optimization of contact materials, 19,21 and a tremendous investment in fundamental understanding of other related PV materials. Figure 1A shows the device stack of a state-of-the-art SnS heterojunction, thin-film solar cell measured in this study, including materials, thicknesses, and deposition techniques for each layer.…”

Section: Photovoltaic Device Fabrication and Characterizationmentioning

confidence: 99%

“…In an exhaustive effort to identify which of these properties may be the limiting factor, many of these properties must be independently measured and modeled. 14,18,19,21 Typically, characterization of each property involves a study of a sample of one or a few of these layers in isolation. This requires that separate samples be fabricated for each measurement, taking additional time (see Figure 1B), and as these materials are not measured in the context of the device in which they will operate, the measurements may not be truly representative of how they will perform after complete processing and during operation.…”

Section: Photovoltaic Device Fabrication and Characterizationmentioning

confidence: 99%

Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation

Brandt

Kurchin

Steinmann

et al. 2017

Joule

Self Cite

View full text Add to dashboard Cite

High-performance computing can greatly improve the workflow of experimentalists in energy materials, through the use of Bayesian inference. This allows us to solve the inverse problem of extracting underlying materials properties through the measurement of the electrical behavior of completed devices. Cheaper, faster measurements can be substituted for longer direct measurements of individual properties, without sacrificing accuracy or precision. We provide a general framework to apply this to other materials systems and devices.

show abstract

Framework to predict optimal buffer layer pairing for thin film solar cell absorbers: A case study for tin sulfide/zinc oxysulfide

Cited by 29 publications

References 32 publications

Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods

Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods

Measurement of contact resistivity at metal-tin sulfide (SnS) interfaces

Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation

Contact Info

Product

Resources

About