Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
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Author(s) • • • • • • • •
Jaramillo, Rafael
Steinmann, Vera
Yang, Chuanxi
Hartman, Katy
Chakraborty, Rupak
Castillo, Mariela Lizet
Gordon, Roy
Poindexter, Jeremy Roger
Buonassisi, Anthony
Date Issued
May 2015
Journal
Journal of Visualized Experiments
Publisher
MyJoVE Corporation
Citation
Jaramillo, Rafael, Vera Steinmann, Chuanxi Yang, Katy Hartman, Rupak Chakraborty, Jeremy R. Poindexter, Mariela Lizet Castillo, Roy Gordon, and Tonio Buonassisi. “Making Record-Efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition.” JoVE no. 99 (2015). © 2015 Journal of Visualized Experiments
Version
Final published version
Abstract
Tin sulfide (SnS) is a candidate absorber material for Earth-abundant, non-toxic solar cells. SnS offers easy phase control and rapid growth by congruent thermal evaporation, and it absorbs visible light strongly. However, for a long time the record power conversion efficiency of SnS solar cells remained below 2%. Recently we demonstrated new certified record efficiencies of 4.36% using SnS deposited by atomic layer deposition, and 3.88% using thermal evaporation. Here the fabrication procedure for these record solar cells is described, and the statistical distribution of the fabrication process is reported. The standard deviation of efficiency measured on a single substrate is typically over 0.5%. All steps including substrate selection and cleaning, Mo sputtering for the rear contact (cathode), SnS deposition, annealing, surface passivation, Zn(O,S) buffer layer selection and deposition, transparent conductor (anode) deposition, and metallization are described. On each substrate we fabricate 11 individual devices, each with active area 0.25 cm[superscript 2]. Further, a system for high throughput measurements of current-voltage curves under simulated solar light, and external quantum efficiency measurement with variable light bias is described. With this system we are able to measure full data sets on all 11 devices in an automated manner and in minimal time. These results illustrate the value of studying large sample sets, rather than focusing narrowly on the highest performing devices. Large data sets help us to distinguish and remedy individual loss mechanisms affecting our devices.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Laboratory for Manufacturing and Productivity
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.3791/52705