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.

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.

This paper addresses a modeling and simulation methodology for analog circuit design with amorphous-GIZO thin-film transistors (TFTs). To reach an effective circuit design flow, with commercially available tools, a TFT model has been first developed with an artificial neural network (ANN). Multilayer perceptron with backpropagation algorithm has been adopted to model the static behavior of the TFT devices, for different aspect ratios. The model was then implemented in Verilog-A, to allow a quick instantiation in circuit. Simulations using Cadence Spectre are performed to validate the model. On a second phase, simulation results of basic analog circuits, with this ANN model, are verified against the actual functional results, namely an adder, subtractor, and current mirror circuit. Results demonstrate not only the ANN model accuracy and compatibility with dc and transient analysis, but also show the a-GIZO TFT capability to perform analog operations.

In wireless power transfer systems, if the driver is not capable of dynamically adapt its own switching frequency, small environmental changes or even slight deviations in circuit parameters may prevent the complete system from working properly when the optimal resonance frequency moves towards new values. In this paper, we propose an adaptive system suitable for underwater wireless applications in sea water. The output voltage is regulated using the wireless power link, avoiding the need for additional wireless interfaces. Our complete system includes the power driver, coupling coils, rectifier, and two micro-controllers. The regulation is accomplished by digital load modulation, observable at the input by means of current sensing at the power supply. Experimental results demonstrate a class-D driver with a series-series resonant topology working in saline water, delivering power between 1.6 and 2.4W. The regulated voltage is 7.5V with error less than 7.2% in the load range of 30 to 37 Ω and 6 to 10V power supply variation. The switching frequency is adjusted within the range of 7 kHz deviation (-7%).

Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution—for the automotive industry—to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant.