Effects of post metallization annealing on Al2O3 atomic layer deposition on n-GaN

The chemical, physical and electrical properties and the robustness of post metallization annealed Al2O3 atomic layers deposited on n-type GaN are investigated in this work. Planar metal insulator capacitors are used to demonstrate a gate-first with following ohmic contacts formation at elevated temperature up to 600 °C process flow. X-ray photoelectron spectroscopy indicates that no new bonds in the Al2O3 layer are formed due to exposure to the elevated annealing temperature. X-ray diffraction measurements show no crystallization of the oxide layer. Atomic force microscopy shows signs of degradation of the sample annealed at 600 °C. Electrical measurements indicate that the elevated annealing temperature results in an increase of the oxide depletion and the deep depletion capacitances simultaneously, that results in a reduction of the flat band voltage to zero, which is explained by fixed oxide charges curing. A forward bias step stress capacitance measurement shows that the total number of induced trapped charges are not strongly affected by the elevated annealing temperatures. Interface trap density of states analysis shows the lowest trapping concentration for the capacitor annealed at 500 °C. Above this temperature, the interface trap density of states increases. When all results are taken into consideration, we have found that the process thermal budget allows for an overlap between the gate oxide post metallization annealing and the ohmic contact formation at 500 °C

Accelerating Neutron Tomography experiments through Artificial Neural Network based reconstruction

Neutron Tomography (NT) is a non-destructive technique to investigate the inner structure of a wide range of objects and, in some cases, provides valuable results in comparison to the more common X-ray imaging techniques. However, NT is time consuming and scanning a set of similar objects during a beamtime leads to data redundancy and long acquisition times. Nowadays NT is unfeasible for quality checking study of large quantities of similar objects. One way to decrease the total scan time is to reduce the number of projections. Analytical reconstruction methods are very fast but under this condition generate streaking artifacts in the reconstructed images. Iterative algorithms generally provide better reconstruction for limited data problems, but at the expense of longer reconstruction time. In this study, we propose the recently introduced Neural Network Filtered Back-Projection (NN-FBP) method to optimize the time usage in NT experiments. Simulated and real neutron data were used to assess the performance of the NN-FBP method as a function of the number of projections. For the first time a machine learning based algorithm is applied and tested for NT image reconstruction problem. We demonstrate that the NN-FBP method can reliably reduce acquisition and reconstruction times and it outperforms conventional reconstruction methods used in NT, providing high image quality for limited datasets

Passivation effects in B doped self-assembled Si nanocrystals

Doping of semiconductor nanocrystals has enabled their widespread technological application in optoelectronics and micro/nano-electronics. In this work, boron-doped self-assembled silicon nanocrystal samples have been grown and characterised using Electron Spin Resonance and photoluminescence spectroscopy. The passivation effects of boron on the interface dangling bonds have been investigated. Addition of boron dopants is found to compensate the active dangling bonds at the interface, and this is confirmed by an increase in photoluminescence intensity. Further addition of dopants is found to reduce the photoluminescence intensity by decreasing the minority carrier lifetime as a result of the increased number of non-radiative processes

GaAs microwave power HBTs for mobile communications

A I W output power (2.8 W/mm) GaAs-based HBT with more than 56 % power added efficiency at 3 V operating bias for use in mobile communications is described. Device layout and technology are optimized to obtain high microwave performance with low thermal resistance and compact chip size. The fabricated sub-cell power HBTs with 3x30 urn2 emitter area yield a maximum current gain of 90 with fmax > 100 GHz. The power cell HBT, which has 12x3x30 um2 emitter area exhibits 3 W (8.4 W/mm) output power and 70 % power added efficiency at VCE=8 V while maintaining high performance at low supply bias of 3 V

Your evidence? Machine learning algorithms for medical diagnosis and prediction

Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues seems to be that understanding and responsibility are concepts that are intrinsically tied to the discursive practice of giving and asking for reasons. The challenge is to find ways to make the outcomes of machine learning algorithms compatible with our discursive practice. This comes down to the claim that we should try to integrate discursive elements into machine learning algorithms. Under the title of "explainable AI" initiatives heading in this direction are already under way. Extensive research in this field is needed for finding adequate solutions