Modelling CdTe thin film growth over realistic time scales

Cadmium Telluride (CdTe) is an excellent material for low-cost, high efficiency thin-film solar cells and holds the record for watts/cost performance. The laboratory record efficiency of CdTe solar cells lags significantly behind the theoretical maximum for the material. This discrepancy is often attributed to defects such as grain boundaries and dislocations. Thus it is important to do research on how these defects are formed during the growth process. Atomistic simulations, such as Molecular Dynamics (MD) and on-the-fly Kinetic Monte Carlo (OTF-KMC), are widely used in partnership with experiments in addressing problems in materials science. In this work we use computer simulation to predict the growth of the sputter deposited CdTe thin film. At the first stage, MD studies of small cluster energetic impacts were carried out by repeatedly depositing CdxTey (x, y = 0, 1) clusters onto different CdTe surfaces with different energies at random positions. The impacts were simulated on Cd- and Te-terminated (100) surfaces and Cd- and Te-terminated (111) surfaces with typical industrial energies varies from 1 to 40 eV at a temperature of 350 K. More than 1,000 simulations have been preformed for each of these cases so as to sample the possible deposition positions and to collect sufficient statistics. The behaviour of deposited clusters under different conditions are studies. To simulate the process of thin film growth is the next stage in this work. We use different techniques to simulate the growth process on different surfaces. OTF-KMC simulations are performed to simulate the thin film growth process on the (111) CdTe surfaces. Starting with several ad-atoms deposited on the surfaces, in each step, the OTF-KMC method searches for all possible atomic movements (transitions) and randomly selects a transition or deposition to execute based on their corresponding rates. The thin film grows with more and more clusters to be deposited onto the surface with numerous ad-atom diffusions. The growth process on the dimerised Te-terminated (100) surface is very interesting. Knowledge of how the Te dimers on the surface split during the growth is gained in the simulations. MD is used to simulate the growth process with an accelerated deposition rate. Several simulations with different deposition energies are performed to see the differences of dissociation of the surface Te dimers. Post-annealing at different temperatures are applied after the growth simulations to find the optimal annealing temperature

Computer vision based techniques for fall detection with application towards assisted living

In this thesis, new computer vision based techniques are proposed to detect falls of an elderly person living alone. This is an important problem in assisted living. Different types of information extracted from video recordings are exploited for fall detection using both analytical and machine learning techniques. Initially, a particle filter is used to extract a 2D cue, head velocity, to determine a likely fall event. The human body region is then extracted with a modern background subtraction algorithm. Ellipse fitting is used to represent this shape and its orientation angle is employed for fall detection. An analytical method is used by setting proper thresholds against which the head velocity and orientation angle are compared for fall discrimination. Movement amplitude is then integrated into the fall detector to reduce false alarms. Since 2D features can generate false alarms and are not invariant to different directions, more robust 3D features are next extracted from a 3D person representation formed from video measurements from multiple calibrated cameras. Instead of using thresholds, different data fitting methods are applied to construct models corresponding to fall activities. These are then used to distinguish falls and non-falls. In the final works, two practical fall detection schemes which use only one un-calibrated camera are tested in a real home environment. These approaches are based on 2D features which describe human body posture. These extracted features are then applied to construct either a supervised method for posture classification or an unsupervised method for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build robust fall detection methods. Extensive evaluation studies are included to confirm the efficiency of the schemes

The energetic impact of small CdxTey clusters on Cadmium Telluride

Cadmium Telluride (CdTe) is an excellent material for low-cost, high efficiency thin film solar cells. It is important to do research on how these defects are formed during the growth process, since defects lower the efficiency of solar cells. In this work we use computer simulation to predict the growth of a sputter deposited CdTe thin film. Single deposition tests have been performed, to study the behaviour of deposited clusters under different conditions. We deposit a CdxTey (x,y = 0,1) cluster onto the (100) and (111) Cd and Te terminated surfaces with energies ranging from 1 to 40 eV. More than 1000 simulations have been performed for each of these cases so as to sample the possible deposition positions and to collect sufficient statistics. The results show that Cd atoms are more readily sputtered from the surface than Te atoms and the sticking probability is higher on Te terminated surfaces than Cd terminated surfaces. They also show that increasing the deposition energy typically leads to an increase in the number of atoms sputtered from the system and tends to decrease the number of atoms that sit on or in the surface layer, whilst increasing the number of interstitials observed

Using atomistic simulations to model Cadmium Telluride thin film growth

Cadmium telluride (CdTe) is an excellent material for low-cost, high efficiency thin film solar cells. It is important to conduct research on how defects are formed during the growth process, since defects lower the efficiency of solar cells. In this work we use computer simulation to predict the growth of a sputter deposited CdTe thin film. On-the-fly kinetic Monte Carlo technique is used to simulate the CdTe thin film growth on the (1 1 1) surfaces. The results show that on the (1 1 1) surfaces the growth mechanisms on surfaces which are terminated by Cd or Te are quite different, regardless of the deposition energy (0.1\sim 10 eV). On the Te-terminated (1 1 1) surface the deposited clusters first form a single mixed species layer, then the Te atoms in the mixed layer moved up to form a new layer. Whilst on the Cd-terminated (1 1 1) surface the new Cd and Te layers are formed at the same time. Such differences are probably caused by stronger bonding between ad-atoms and surface atoms on the Te layer than on the Cd layer

State dependent multiple model-based particle filtering for ballistic missile tracking in a low-observable environment

This paper proposes a new method for tracking the whole trajectory of a ballistic missile (BM), in a low-observable environment with ‘imperfect’ sensor measurement incorporating both miss detection and false alarms. A hybrid system with state dependent transition probabilities is proposed where multiple state models represent the ballistic missile movement during different phases; and domain knowledge is exploited to model the transition probabilities between different flight phases in a state-dependent way. The random finite set (RFS) is adopted to model radar sensor measurements which include both miss detection and false alarms. Based on the proposed hybrid modeling system and the RFS represented sensor measurements, a state dependent interacting multiple model particle filtering method integrated with a generalized measurement likelihood function is developed for the BM tracking. Comprehensive simulation studies show that the proposed method outperforms the traditional ones for the BM tracking, with more accurate estimations of flight mode probabilities, positions and velocities

An unsupervised acoustic fall detection system using source separation for sound interference suppression

We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods

Reaction pathways in atomistic models of thin film growth

The atomistic processes that form the basis of thin film growth often involve complex multi-atom movements of atoms or groups of atoms on or close to the surface of a substrate. These transitions and their pathways are often difficult to predict in advance. By using an adaptive kinetic Monte Carlo (AKMC) approach many complex mechanisms can be identified so that the growth processes can be understood and ultimately controlled. Here the AKMC technique is briefly described along with some special adaptions that can speed up the simulations when, for example, the transition barriers are small. Examples are given of such complex processes that occur in different material systems especially for the growth of metals and metallic oxides

New multiple target tracking strategy using domain knowledge and optimisation - supporting data (single target tracking scenario)

Related data for SMC paper 'New Multiple Target Tracking Strategy Using Domain Knowledge and Optimisation'. This is data related to the 'single target tracking scenario'

New multiple target tracking strategy using domain knowledge and optimisation - supporting data (multiple target tracking scenario)

Related data for SMC paper 'New Multiple Target Tracking Strategy Using Domain Knowledge and Optimisation'. This data related to the multiple target tracking scenario

Modelling the deposition process on the CdTe/CdS interface

CdTe is an excellent material for low-cost, high efficiency thin film solar cells and holds the record for Watts/$ performance [1, 2]. Defects such as grain boundaries and dislocations lower the efficiency of CdTe solar cells [3], thus it is important to do research on how these defects are formed during the growth process, especially on the interfaces of different materials. In this work we use computer simulation to predict the growth of a sputter deposited CdTe thin film on the CdS surfaces. Single deposition tests have been performed, to study the behaviour of deposited clusters under different conditions. We deposit a CdxTey (x; y = 0; 1) cluster onto the wurtzite (111) Cd and S terminated CdS surfaces with energies ranging from 1 to 40 eV. More than 1,200 simulations have been performed for each of these cases so as to sample the possible deposition positions and to collect sufficient statistics. The results show that Cd atoms are more readily sputtered from the surface than Te atoms and the sticking probability is higher on S terminated surfaces than Cd terminated surfaces. They also show that increasing the deposition energy typically leads to an increase in the number of deposited atoms replacing surface atoms and tends to decrease the number of atoms that sit on the surface layer, whilst increasing the number of interstitials observed