Abstract
This paper analyzes transparent two-TFT current mirrors using a-GIZO TFTs with different mirroring ratios. In order to achieve a high mirroring ratio, the output TFT in the circuit employed a fingered structure layout to minimize area and overlap capacitance. The analysis of the current mirrors is performed in three phases. In the first, a radial basis function based (RBF) model is developed using measured data from fabricated TFTs on the same chip. Then, in the second phase, the RBF model is implemented in Verilog-A that is used to simulate two-TFT current mirrors with different mirroring ratios. The simulations are carried out using Cadence spectre simulator. In the third phase, simulation results are validated with the measured response from the fabricated circuits.

Abstract
This paper characterizes transparent current mirrors with n-type amorphous gallium-indium-zinc-oxide (a-GIZO) thin-film transistors (TFTs). Two-TFT current mirrors with different mirroring ratios and a cascode topology are considered. A neural model is developed based on the measured data of the TFTs and is implemented in Verilog-A; then it is used to simulate the circuits with Cadence Virtuoso Spectre simulator. The simulation outcomes are validated with the fabricated circuit response. These results show that the neural network can model TFT accurately, as well as the current mirroring ability of the TFTs.

Abstract
The present work addresses the design of an electronic system for powering autonomous underwater vehicles (AUVs). We report the study and implementation of a system developed for transmitting and receiving wireless power in water from a docking station to an AUV. A simplified analysis is presented regarding the operation of a class-D power driver in a series-series inductive resonant coupling topology. We further investigate the compromise between link efficiency and power delivery of the system to circumvent the reduced power output capability. Simulation results are provided as well as measurement data validated using circuit prototypes in an experimental setup with a structure similar to the docking station of the AUV filled with salt water.

Abstract
Monte Carlo (MC) techniques are widely applied to check a design on its robustness and for estimating the production yield of integrated circuits. Using standard random MC and the sample yield for estimation, a very large number of samples is required for accurate verification, especially if a high yield is desired. This can make MC extremely time consuming, but if the data follows a normal Gaussian distribution a much faster yield prediction is possible by using the well-known C-PK method. We extended this specification-distance-based scheme for the far more difficult general non-normal case by three different means, ending up in a new generalized process capability index named C-GPK. First, we apply parametric modeling only to the specification-sided distribution part. This way any difficulties in distribution parts that actually have little yield impact do not degrade the model fit anymore. Second, to improve the parametric model we introduce a new tail parameter t. Third, to allow modeling of difficult asymmetrical, multimodal or flat distributions we also introduce a new reference location parameter instead of using the mean. An advantage of improving MC this way is that-in opposite to many other MC enhancements (like importance sampling)-the performance of the C-GPK is not negatively impacted by design complexity. We described the formulation of the C-GPK and derived confidence intervals using an advanced bootstrap scheme. We verified the performance against the sample yield and C-PK for a representative set of distributions, including real production data and MC data from the design of a CMOS operational amplifier and other circuits.

Abstract

Abstract
Alzheimer's disease is a major cause of disability in older people and because of its nature and symptoms it is a huge weight on the caregivers and health system. The aim of this project, developed at Fraunhofer Portugal, is to create a remote monitoring system for patients suffering from Alzheimer's disease. This system comprises a device able to monitor the environment temperature and humidity, the patients location, using GPS, as well as the patient's movements, including falls. The device sends this information to the caregiver via SMS, and also to a server, where it is stored in a database developed in this work, and which the caregiver can access via a smartphone, using an application also developed in this project. This system therefore affords a close surveillance of the patient's status as well as access to historical data of the patient's information.