Micropatterning of TiO2 thin films by MOCVD and study of their growth tendency.

In this work, we studied the growth tendency of TiO2 thin films deposited on a narrow-stripe area (<10 µm). TiO2 thin films were selectively deposited on OTS patterned Si(100) substrates by MOCVD. The experimental data showed that the film growth tendency was divided into two behaviors above and below a line patterning width of 4 µm. The relationship between the film thickness and the deposited area was obtained as a function of f(x) = a[1 - e((-bx))]c. To find the tendency of the deposition rate of the TiO2 thin films onto the various linewidth areas, the relationship between the thickness of the TiO2 thin film and deposited linewidth was also studied. The thickness of the deposited TiO2 films was measured from the alpha-step profile analyses and cross-sectional SEM images. At the same time, a computer simulation was carried out to reveal the relationship between the TiO2 film thickness and deposited line width. The theoretical results suggest that the mass (velocity) flux in flow direction is directly affected to the film thickness.

Real-time gesture interface based on event-driven processing from stereo silicon retinas.

We propose a real-time hand gesture interface based on combining a stereo pair of biologically inspired event-based dynamic vision sensor (DVS) silicon retinas with neuromorphic event-driven postprocessing. Compared with conventional vision or 3-D sensors, the use of DVSs, which output asynchronous and sparse events in response to motion, eliminates the need to extract movements from sequences of video frames, and allows significantly faster and more energy-efficient processing. In addition, the rate of input events depends on the observed movements, and thus provides an additional cue for solving the gesture spotting problem, i.e., finding the onsets and offsets of gestures. We propose a postprocessing framework based on spiking neural networks that can process the events received from the DVSs in real time, and provides an architecture for future implementation in neuromorphic hardware devices. The motion trajectories of moving hands are detected by spatiotemporally correlating the stereoscopically verged asynchronous events from the DVSs by using leaky integrate-and-fire (LIF) neurons. Adaptive thresholds of the LIF neurons achieve the segmentation of trajectories, which are then translated into discrete and finite feature vectors. The feature vectors are classified with hidden Markov models, using a separate Gaussian mixture model for spotting irrelevant transition gestures. The disparity information from stereovision is used to adapt LIF neuron parameters to achieve recognition invariant of the distance of the user to the sensor, and also helps to filter out movements in the background of the user. Exploiting the high dynamic range of DVSs, furthermore, allows gesture recognition over a 60-dB range of scene illuminance. The system achieves recognition rates well over 90% under a variety of variable conditions with static and dynamic backgrounds with naïve users.

Fast neuromorphic sound localization for binaural hearing aids.

We report on the neuromorphic sound localization circuit which can enhance the perceptual sensation in a hearing aid system. All elements are simple leaky integrate-and-fire neuron circuits with different parameters optimized to suppress the impacts of synaptic circuit noises. The detection range and resolution of the proposed neuromorphic circuit are 500 us and 5 us, respectively. Our results show that, the proposed technique can localize a sound pulse with extremely narrow duration (∼ 1 ms) resulting in real-time response.