Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl’s neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task.

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This paper presents the high-level of stability of voltage references operated in alternating current (AC) and based on the pull-in effect in split-fingers MEMS architectures. Our work deals with the design and fabrication of new MEMS devices and presents results of both electrical and mechanical characterizations as well as the development of an optimized read-out electronics showing a mature stage of this technology to be rapidly implemented in various applications. The new aspects in this paper are related to the new architecture of the MEMS allowing to minimize the effect of leakage capacitances on the stability of the voltage reference and to avoid to compensate any "built-in voltage" generated at metal-semiconductor interfaces.