Effect of hydrostatic pressure on the current-voltage characteristics of GaN∕AlGaN∕GaN heterostructure devices

The current-voltage characteristics of n-GaN∕u-AlGaN∕n-GaN heterostructure devices are investigated for potential pressure sensor applications. Model calculations suggest that the current decreases with pressure as a result of the piezoelectric effect, and this effect becomes more significant with thicker AlGaN layers and increasing AlN composition. The change in current with pressure is shown to be highly sensitive to the change in interfacial polarization charge densities. The concept is verified by measuring the current versus voltage characteristics of an n-GaN∕u-Al0.2Ga0.8N∕n-GaN device under hydrostatic pressure over the range of 0–5 kbars. The measured current is found to decrease approximately linearly with applied pressure in agreement with the model results. A gauge factor, which is defined as the relative change in current divided by the in-plane strain, approaching 500 is extracted from the data, demonstrating the considerable potential of these devices for pressure sensing applications

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data.

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results