In Situ Process Monitoring and Multichannel Imaging for Vacuum‐Assisted Growth Control of Inkjet‐Printed and Blade‐Coated Perovskite Thin‐Films

Schackmar, Fabian; Laufer, Felix; Singh, Roja; Farag, Ahmed; Eggers, Helge; Gharibzadeh, Saba; Nejand, Bahram Abdollahi; Lemmer, Uli; Hernandez‐Sosa, Gerardo; Paetzold, Ulrich W.

doi:10.1002/admt.202201331

Cited by 8 publications

(18 citation statements)

References 78 publications

(115 reference statements)

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“…For areas with thicker wet films and hence more material, the film takes longer to dry, and therefore the PL signal onset is delayed when compared to areas with thinner wet films. [ 60 ] In addition to the PL onset time, the transients assigned to the clusters also differ regarding the height of the initial maximum (see Figure 4B). The transients of cluster 0 have low initial maximal PL peaks.…”

Section: Resultsmentioning

confidence: 99%

“…This leads to spatial differences in charge carrier extraction and therefore to locally different quenching of the PL. [ 60 ] In addition to differences in absolute PL intensity, the four clusters also differ regarding the relative behavior over time (see Figure 4C). The transients of cluster 0 with low absolute PL intensity show merely a small relative decrease over time while the relative decrease after the initial maximum gets steeper with increasing cluster number.…”

Section: Resultsmentioning

confidence: 99%

“…PSC Fabrication: The used materials and the fabrication process are based on the ones described in detail in the authors' group's previous work described in Ref. [60].…”

Section: Methodsmentioning

confidence: 99%

“…After the deposition of the perovskite layer, the samples were placed in a self-built vacuum chamber (see Ref. [60] for more details) which was then evacuated for 3 min. After completion of the vacuum-quenching process, the chamber was vented with ambient air and the samples were annealed for 30 min at 150 °C.…”

Section: Methodsmentioning

confidence: 99%

“…For the blade coating of the perovskite layer, the parameters were changed to 25 μL ink volume and 25 mm s −1 blading speed. After the deposition of the perovskite layer, the samples were placed in a self‐built vacuum chamber (see Ref [60]. for more details) which was then evacuated for 3 min.…”

Section: Methodsmentioning

confidence: 99%

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Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

et al. 2023

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technology demonstrates outstanding power conversion efficiencies (PCEs), exceeding 25%. [3] Despite numerous favorable optoelectronic properties of perovskite semiconductors, four key challenges remain and delay the successful commercialization of perovskite solar cells (PSCs): 1) the long-term stability, 2) the toxicity of the contained lead, 3) upscaling to large-areas, and 4) unlocking cost-effective, reliable large-scale production (high throughput and high yield). [2,4] Traditional efforts in material science and device engineering in the field are based on countless trialand-error experiments. However, these approaches for material discovery, process development, characterization, full device evaluation, and stability testing are often complicated, expensive, laborious, and time-consuming given the large experimental parameter space. [5] These drawbacks motivate the implementation of autonomous experimentation methods and data-driven techniques like machine learning (ML). [6,7] In an increasing number of research fields, ML methods are employed to identify yet undiscovered correlations and to provide insights into fundamental working principles. Besides pattern extraction, ML can be utilized to make classifications or predictions and to uncover new insights into the studied data. For this reason, ML algorithms are successfully adopted to an increasing number of applications in materials science, [8][9][10][11] encompassing,

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…PSC Fabrication: The used materials and the fabrication process are based on the ones described in detail in the authors' group's previous work described in Ref. [60].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

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Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI

Klein,

Ziegler,

Laufer

et al. 2023

Advanced Materials

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Large‐area processing of perovskite semiconductor thin‐films is complex and evokes unexplained variance in quality, posing a major hurdle for the commercialization of perovskite photovoltaics. Advances in scalable fabrication processes are currently limited to gradual and arbitrary trial‐and‐error procedures. While the in‐situ acquisition of photoluminescence videos has the potential to reveal important variations in the thin‐film formation process, the high dimensionality of the data quickly surpasses the limits of human analysis. In response, this study leverages deep learning and explainable artificial intelligence (XAI) to discover relationships between sensor information acquired during the perovskite thin‐film formation process and the resulting solar cell performance indicators, while rendering these relationships humanly understandable. We further show how gained insights can be distilled into actionable recommendations for perovskite thin‐film processing, advancing towards industrial‐scale solar cell manufacturing. Our study demonstrates that XAI methods will play a critical role in accelerating energy materials science.This article is protected by copyright. All rights reserved

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Modeling and Fundamental Dynamics of Vacuum, Gas, and Antisolvent Quenching for Scalable Perovskite Processes

Ternes,

Laufer,

Paetzold

2024

Advanced Science

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Hybrid perovskite photovoltaics (PVs) promise cost‐effective fabrication with large‐scale solution‐based manufacturing processes as well as high power conversion efficiencies. Almost all of today's high‐performance solution‐processed perovskite absorber films rely on so‐called quenching techniques that rapidly increase supersaturation to induce a prompt crystallization. However, to date, there are no metrics for comparing results obtained with different quenching methods. In response, the first quantitative modeling framework for gas quenching, anti‐solvent quenching, and vacuum quenching is developed herein. Based on dynamic thickness measurements in a vacuum chamber, previous works on drying dynamics, and commonly known material properties, a detailed analysis of mass transfer dynamics is performed for each quenching technique. The derived models are delivered along with an open‐source software framework that is modular and extensible. Thereby, a deep understanding of the impact of each process parameter on mass transfer dynamics is provided. Moreover, the supersaturation rate at critical concentration is proposed as a decisive benchmark of quenching effectiveness, yielding ≈ 10−3 − 10−1s−1 for vacuum quenching, ≈ 10−5 − 10−3s−1 for static gas quenching, ≈ 10−2 − 100s−1 for dynamic gas quenching and ≈ 102s−1 for antisolvent quenching. This benchmark fosters transferability and scalability of hybrid perovskite fabrication, transforming the “art of device making” to well‐defined process engineering.

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In Situ Process Monitoring and Multichannel Imaging for Vacuum‐Assisted Growth Control of Inkjet‐Printed and Blade‐Coated Perovskite Thin‐Films

Cited by 8 publications

References 78 publications

Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI

Modeling and Fundamental Dynamics of Vacuum, Gas, and Antisolvent Quenching for Scalable Perovskite Processes

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