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•Process data is generated by a dynamic, closed-loop milling circuit simulation.•Data-based methods for fault detection and root cause identification are evaluated.•An approach to ...quantify economic benefit for early fault diagnosis is presented.•The approach is suggested for robust assessment of benefit in industrial processes.•The dynamic simulation and data have been shared online to promote further analysis.
The early detection and root cause identification of abnormal events in industrial processes is important, to allow for timely corrective actions, ensuring continued economic operation. This paper investigates the application of statistical fault detection methods, in conjunction with process topology data-driven techniques for root cause analysis, to a simulated milling circuit. Two faults (faulty particle size analyser and rapid mill liner wear) were simulated, and the statistical monitoring techniques tested. Fault detection proved accurate, and variables closely associated with the faults were identified by the root cause analysis. The need to further formalise the selection of data for process topology generation for root cause analysis was highlighted. The milling circuit simulation and fault data has been made available as a resource for future research. Economic performance factors were developed to quantify the impact of the faults and motivate for fault detection and diagnosis.
Argon plasma sputter etching-induced electronic defects in boron doped, strained p-type Si
1−xGe
x alloys with
x = 0 and 0.05 have been investigated by deep level transient spectroscopy (DLTS). Four ...defects with discrete energy levels, ranging from 0,22–0.55 eV above the valence band, were introduced in p-Si during sputtering. These defects are compared to those introduced during electron beam deposition (EB), alpha particle irradiation and Ar ion beam etching. The most prominent defects in Ar plasma etched samples have similar efectronic properties as a defects detected after electron beam, Ar ion beam etching and alpha particle irradiation. The main defects detected in p-Si was also observed in p-Si
0.95Ge
0.05. One of the dominating peaks has been correlated to the interstitial carbon-interstitial oxygen pair. The decrease in activation energy of this defect for increasing Ge-content from
x = 0–0.05 followed the same variation as the band gap in Si
1−xGe
x/Si. Its energy level position relative to the conduction band is therefore the same for
x = 0 and 0.05 indicating that it is pinned to the conduction band. Defect concentration depth profiling revealed that the main defect introduced during argon plasma sputtering and Ar ion beam etching is located very close to the surface and is deeper than that detected after EB deposition.
Schottky barrier height modification (BHM) resulting from 0.5 keV He-ion bombardment of n- and p-GaAs (doping ∼10
16 cm
−3) is investigated for doses between 5
×
10
10 and 5
×
10
14 cm
−2. ...Current–voltage measurements reveal a decrease (0.95–0.63 eV) and an increase (0.63–0.70) in barrier height, for n- and p-GaAs, respectively, for the dose above. Electronic properties of defects introduced are investigated through DLTS, current–voltage (
I–
V) and capacitance–voltage (
C–
V) measurements. Furthermore, we compare defects from 0.5 keV and MeV He-ion irradiation in both n- and p-GaAs. Annealing n-GaAs reveal a correlation between the BHM and the density of He-ion induced defects.
SiGe heterostructures with their associated geometries and properties promise a novel generation of Si-based devices. Surface processing and, in particular, dry or plasma etching of semiconductors is ...a key technology for producing optoelectronic integrated circuits and high speed electronic devices. We have used deep-level transient spectroscopy (DLTS) in an investigation of the electronic properties of defects introduced in n-Si 1 −xGex (x = 0.00 to 0.25) during 1 keV helium-ion etching (fluence = 1 × 1012 cm²) prior to the deposition of gold Schottky barrier diodes (SBDs). Six electron defects (EHel-EHe6) were detected after this processing stage. The defects detected after etching are compared to those introduced by 5.4 MeV alpha-particle (α-) irradiation and, also, radio frequency (rf) sputter-deposition of Au SBDs on material from the same wafer. Four of the electron defects (EHel, EHe2, EHe4, and EHe6) are detected in Si. The remaining two defects (EHe3 and EHe5) are only detected in material containing germanium. It was noted that defects introduced during the He-ion etch process have the same DLTS “signatures” as defects after the sputter deposition process, but none were the same as those introduced during the α-particle irradiation. The influence of increased Ge content on DLTS peak shape and positions is also illustrated and discussed.
The distance from academic research output to industrial implementation is often daunting, costly, and delays the return on research investment for industrial sponsors. Operational performance ...monitoring techniques, infrastructure and tools are of pivotal importance to efficient and effective process engineering plants, but new research output typically requires extensive development before deployment. Implementation, in terms of leading technology stacks, of research output can bridge part of the distance between research and deployment. This paper considers a research & development strategy that accelerates technology transfer by implementation of research output as concept demonstrators for operational performance monitoring in process plants.
The isotopic composition of carbon and nitrogen as well as of strontium in animal bone is related to the environment in which the animal lived1-6. It can be assumed that this is also the case for ...lead isotopes. In theory, therefore, we have a way of pinpointing the origin of elephant ivory, which may be of value in conservation. Here we report that by analysing the isotope ratios of these elements, a clear distinction between several different populations of the African elephant can be made.
Image-based soft sensors are of interest in process industries due to their cost-effective and non-intrusive properties. Unlike most multivariate inputs, images are highly dimensional, requiring the ...use of feature extractors to produce lower dimension representations. These extractors have a large impact on final sensor performance. Traditional texture feature extraction methods consider limited feature types, requiring expert knowledge to select and may be sensitive to changing imaging conditions. Deep learning methods are an alternative which does not suffer these drawbacks. A specific deep learning method, Convolutional Neural Networks (CNNs), mitigates the curse of dimensionality inherent in fully connected networks but must be trained, unlike other feature extractors. This allows both textural and spectral features to be discovered and utilised. A case study consisting of platinum flotation froth images at four distinct platinum-grades was used. Extracted feature sets were used to train linear and nonlinear soft sensor models. The quality of CNN features was compared to those from traditional texture feature extraction methods. Performance of CNNs as feature extractors was found to be competitive, showing similar performance to the other texture feature extractors. However, the dataset also exhibits strong spectral features, complicating comparison between texture feature extractors. The results gathered do not provide sufficient information to distinguish between the types of features detected by the CNN and further investigation is required.
Ion beam etching with noble gases is routinely used in the fabrication of submicron-scale structures. Low energy ion bombardment, however, introduces near surface defects in semiconductors, which may ...alter their electrical properties, and may hence govern the properties of devices fabricated on these semiconductors. We have employed deep level transient spectroscopy (DLTS) to characterise the defects induced by low energy (1 keV) He- and Ar-ion bombardment of epitaxially grown n-type Si, each of which introduced several prominent electron traps. We found that the DLTS spectrum of low energy Ar-ion bombarded Si contained a number of peaks that that were not present in the low energy He-ion bombarded sample. The DLTS spectra of both low-energy bombarded samples displayed several peaks in addition to the V
2, VO and VP centres which are generally observed in the spectrum obtained from high energy (5.4 MeV) α-particle implanted Si. We attribute the differences in the defects induced by low energy He and Ar ions and high energy He ions to their different stopping powers, i.e. the different rate of energy loss in Si, as well as the inclusion of He and Ar ions in vacancy complexes.
Hydrogen-ion implantation was studied for Mg-doped hexagonal GaN grown on sapphire. Low temperature photoluminescence spectroscopy (PL) shows two significant features; the implantation-annealing ...induced yellow band (YL) and a remarkable sharp excitonic peak. In the region 1.73–1.79 eV well resolved optical transitions were observed, which resemble the well known R
1 and R
2 emission bands from Cr
3+ in Al
2O
3 (ruby). Structural and electronic changes were monitored by inelastic light scattering (ILS) spectroscopy. At high implantation dose and high annealing temperature we observed well resolved bands at 320, 380 and 640 cm
−1. The latter band ‘splits’ into 645 and 672 bands at the highest implantation dose. Additionally, implantation-annealing induced band was observed at 430 cm
−1. This band was not observed before. Besides these, four of the six Raman allowed modes are present in the spectra: 2A
1, E
1 and E
2. Second order Raman spectroscopy yields several bands in the region 860–1470 cm
−1. On the high energy side of the spectra, we monitor luminescence bands at 1.878, 1.85, 1.836 eV and for the first time observed a 1.8 eV band. Using a group-theoretical approach we assign symmetries of the first order phonons at
k=0 as well as some experimentally observed second order symmetry allowed modes.
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