Crystalline silicon comprises 90% of the global photovoltaics (PV) market and has sustained a nearly 30% cumulative annual growth rate, yet comprises less than 2% of electricity capacity. To sustain this growth trajectory, continued cost and capital expenditure (capex) reductions are needed. Thinning the silicon wafer well below the industry-standard 160 µm, in principle reduces both manufacturing cost and capex, and accelerates economicallysustainable expansion of PV manufacturing. In this Analysis piece, we explore two questions surrounding adoption of thin silicon wafers: (a) what are the market benefits of thin wafers? (b) what are the technological challenges to adopt thin wafers? In this Analysis, we re-evaluate the benefits and challenges of thin Si for current and future PV modules using a comprehensive technoeconomic framework that couples device simulation, bottom-up cost modeling, and a sustainable cash-flow growth model. When adopting an advanced technology concept that features sufficiently good surface passivation, the same high efficiencies are achievable for both 50-µm wafers and 160-µm ones. We then quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module manufacturing capex, module cost, and levelized cost of electricity (LCOE) for utility PV systems. Particularly, LCOE favors thinner wafers for all investigated device architectures, and can potentially be reduced by more than 5% from the value of 160-µm wafers. With further improvements in module efficiency, an advanced device concept with 50-µm wafers could potentially reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%. Furthermore, we apply a sustainable growth model to investigate PV deployment scenarios in 2030. It is found that the state-of-theart industry concept could not achieve the climate targets even with very aggressive financial scenarios, therefore the capex reduction benefit of thin wafers is advantageous to facilitate faster PV adoption. Lastly, we discuss the remaining technological challenges and areas for innovation to enable high-yield manufacturing of high-efficiency PV modules with thin Si wafers.
Broader ContextClimate change is among the greatest challenges facing humankind today. Given the urgency of transitioning to a carbon-neutral energy system, we need to accelerate the deployment of existing renewable technology in the near term. With rapid technological progress and cost decline, silicon photovoltaics (PV) modules is a proven technology to be deployed to a multi-terawatt scale by 2030. Despite the high growth rate in the past decade, the capital-intense nature of silicon PV manufacturing hinders the sustainable growth of the industry. Today, the most significant contribution to capital expenditure (capex) of PV module fabrication still comes from silicon wafer itself. Reducing wafer thickness would have a proportionate effect on wafer and poly capex; however, wafer thickness reduction has been much slower than anticipated. This study revisits the concept of wafer thinning...
While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.
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