Progress on optimization of the solar cell fabrication process by impurity-to-efficiency predictive simulator

To optimize the last high temperature step of a standard solar cell fabrication process (the contact cofiring step), the aluminium gettering is incorporated in the Impurity-to-Efficiency simulation tool, so that it models the phosphorus and aluminium co-gettering effect on iron impurities. The impact of iron on the cell efficiency will depend on the balance between precipitate dissolution and gettering. Gettering efficiency is similar in a wide range of peak temperatures (600-850 ºC), so that this peak temperature can be optimized favoring other parameters (e.g. ohmic contact). An industrial co-firing step can enhance the co-gettering effect by adding a temperature plateau after the peak of temperature. For highly contaminated materials, a short plateau (menor que 2 min) at low temperature (600 ºC) is shown to reduce the dissolved iron.

Air-steam gasification of sewage sludge in a bubbling bed reactor: Effect of alumina as a primary catalyst

Numerous references can be found in scientific literature regarding biomass gasification. However, there are few works related to sludge gasification. A study of sewage sludge gasification process in a bubbling fluidised bed gasifier on a laboratory scale is here reported. The aim was to find the optimum conditions for reducing the production of tars and gain more information on the influx of different operating variables in the products resulting from the gasification of this waste. The variables studied were the equivalence ratio (ER), the steam-biomass ratio (SB) and temperature. Specifically, the ER was varied from 0.2 to 0.4, the SB from 0 to 1 and the temperature from 750 °C (1023 K) to 850 °C (1123 K). Although it was observed that tar production could be considerably reduced (up to 72%) by optimising the gasification conditions, the effect of using alumina (aluminium oxide, of proven efficacy in destroying the tar produced in biomass gasification) as primary catalyst in air and air-steam mixture tests was also verified. The results show that by adding small quantities of alumina to the bed (10% by weight of fed sludge) considerable reductions in tar production can be obtained (up to 42%) improving, at the same time, the lower heating value (LHV) of the gas and carbon conversion.

Moonlight and shelter cause differential seed selection and removal by rodents

Various environmental factors may influence the foraging behaviour of seed dispersers which could ultimately affect the seed dispersal process. We examined whether moonlight levels and the presence or absence of rodentshelter affect rodentseedremoval (rate, handling time and time of removal) and seedselection (size and species) among seven oak species. The presence or absence of safe microhabitats was found to be more important than moonlight levels in the removal of seeds. Bright moonlight caused a different temporal distribution of seedremoval throughout the night but only affected the overall removal rates in open microhabitats. Seeds were removed more rapidly in open microhabitat (regardless of the moon phase), decreasing the time allocated to seed discrimination and translocation. Only in open microhabitats did increasing levels of moonlight decrease the time allocated to selection and removal of seeds. As a result, a more precise seedselection was made under shelter, owing to lower levels of predation risk. Rodent ranking preference for species was identical between full/new moon in shelter but not in open microhabitats. For all treatments, species selection by rodents was much stronger than size selection. Nevertheless, heavy seeds, which require more energy and time to be transported, were preferentially removed under shelter, where there is no time restriction to move the seeds. Our findings reveal that seedselection is safety dependent and, therefore, microhabitats in which seeds are located (sheltered versus exposed) and moonlight levels in open areas should be taken into account in rodent food selection studies.

TCAD for PV: a fast method for accurately modelling metal impurity evolution during solar cell processing

Coupled device and process silumation tools, collectively known as technology computer-aided design (TCAD), have been used in the integrated circuit industry for over 30 years. These tools allow researchers to quickly converge on optimized devide designs and manufacturing processes with minimal experimental expenditures. The PV industry has been slower to adopt these tools, but is quickly developing competency in using them. This paper introduces a predictive defect engineering paradigm and simulation tool, while demonstrating its effectiveness at increasing the performance and throughput of current industrial processes. the impurity-to-efficiency (I2E) simulator is a coupled process and device simulation tool that links wafer material purity, processing parameters and cell desigh to device performance. The tool has been validated with experimental data and used successfully with partners in industry. The simulator has also been deployed in a free web-accessible applet, which is available for use by the industrial and academic communities.

Transductive neurofuzzy-based torque control of a milling process: results of a case study

This paper presents the design and implementation of an intelligent control system based on local neurofuzzy models of the milling process relayed through an Ehternet-based application. Its purpose is to control the spindle torque of a milling process by using an internal model control paradigm to modify the feed rate in real time. The stabilization of cutting cutting torque is especially necessary in milling processes such as high-spedd roughing of steel moulds and dies tha present minor geometric uncertainties. Thus, maintenance of the curring torque increaes the material removal rate and reduces the risk of damage due to excessive spindle vibration, a very sensitive and expensive component in all high-speed milling machines. Torque control is therefore an interesting challenge from an industrial point of view.

One-dimensional linear analysis of the liquid injection or removal in a liquid bridge

The filling-withdrawal process of a long liquid bridge is analyzed using a one-dimensional linearized model for the dynamics of the liquid column. To carry out this study, a well-known standard operational method (Laplace transform) has been used, and time variation of both liquid velocity field and interface shape are obtained.

How do Impurity Inclusions Influence the Mechanical Properties of Multicrystalline Silicon?

The purpose of this research is to characterise the mechanical properties of multicrystalline silicon for photovoltaic applications that was crystallised from silicon feedstock with a high content of several types of impurities. The mechanical strength, fracture toughness and elastic modulus were measured at different positions within a multicrystalline silicon block to quantify the effect of impurity segregation on these mechanical properties. The microstructure and fracture surfaces of the samples was exhaustively analysed with a scanning electron microscope in order to correlate the values of mechanical properties with material microstructure. Fracture stresses values were treated statistically via the Weibull statistics. The results of this research show that metals segregate to the top of the block, produce moderate microcracking and introduce high thermal stresses. Silicon oxide is produced at the bottom part of the silicon block, and its presence significantly reduces the mechanical strength and fracture toughness of multicrystalline silicon due to both thermal and elastic mismatch between silicon and the silicon oxide inclusions. Silicon carbide inclusions from the upper parts of the block increase the fracture toughness and elastic modulus of multicrystalline silicon. Additionally, the mechanical strength of multicrystalline silicon can increase when the radius of the silicon carbide inclusions is smaller than ~10 µm. The most damaging type of impurity inclusion for the multicrystalline silicon block studied in this work was amorphous silicon oxide. The oriented precipitation of silicon oxide at grain and twin boundaries eases the formation of radial cracks between inclusions and decreases significatively the mechanical strength of multicrystalline silicon. The second most influencing type of impurity inclusions were metals like aluminium and copper, that cause spontaneous microcracking in their surroundings after the crystallisation process, therefore reducing the mechanical response of multicrystalline silicon. Therefore, solar cell producers should pay attention to the content of metals and oxygen within the silicon feedstock in order to produce solar cells with reliable mechanical properties.

Building intuition of iron evolution during solar cell processing through analysis of different process models

An important aspect of Process Simulators for photovoltaics is prediction of defect evolution during device fabrication. Over the last twenty years, these tools have accelerated process optimization, and several Process Simulators for iron, a ubiquitous and deleterious impurity in silicon, have been developed. The diversity of these tools can make it difficult to build intuition about the physics governing iron behavior during processing. Thus, in one unified software environment and using self-consistent terminology, we combine and describe three of these Simulators. We vary structural defect distribution and iron precipitation equations to create eight distinct Models, which we then use to simulate different stages of processing. We find that the structural defect distribution influences the final interstitial iron concentration ([Fe-i]) more strongly than the iron precipitation equations. We identify two regimes of iron behavior: (1) diffusivity-limited, in which iron evolution is kinetically limited and bulk [Fe-i] predictions can vary by an order of magnitude or more, and (2) solubility-limited, in which iron evolution is near thermodynamic equilibrium and the Models yield similar results. This rigorous analysis provides new intuition that can inform Process Simulation, material, and process development, and it enables scientists and engineers to choose an appropriate level of Model complexity based on wafer type and quality, processing conditions, and available computation time.