The ongoing research on and development of increasingly intelligent artificial systems propels the need for bio inspired pressure sensitive spiking circuits. Here we present an adapting and spiking tactile sensor, based on a neuronal model and a piezoelectric field-effect transistor (PiezoFET). The piezoelectric sensor device consists of a metal-oxide semiconductor field-effect transistor comprising a piezoelectric aluminium-scandium-nitride (AlxSc1−xN) layer inside of the gate stack. The so augmented device is sensitive to mechanical stress. In combination with an analogue circuit, this sensor unit is capable of encoding the mechanical quantity into a series of spikes with an ongoing adaptation of the output frequency. This allows for a broad application in the context of robotic and neuromorphic systems, since it enables said systems to receive information from the surrounding environment and provide encoded spike trains for neuromorphic hardware. We present numerical and experimental results on this spiking and adapting tactile sensor.

Piezoelectric materials have been introduced to transistor gate stacks to improve MOSFET behaviour and develop sensor applications. In this work, we present an approach to a partly industrial field effect transistor, with a gate stack based upon low temperature AlN. Using the piezoelectric effect of the nitrogen-polar AlN, we are able to drive the transistor by inducing strain across the device. To ensure maximum sensitivity, the piezoelectric material is placed as closely to the transistor channel as possible and the transistor is operated in the most sensitive part of the sub-threshold regime. This allows the detection of different magnitudes of force applied to the device and to easily distinguish between them. The created sensor was analysed using XRD, current-voltage and specific force application measurements. Furthermore, the continuous response to periodic low frequency stimulation is investigated. Therefore, we introduce a highly scalable device with a wide range of application possibilities, ranging from varying sensor systems to energy harvesting.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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We investigate the effect of a memristive element on the dynamics of a chaotic system. For this purpose, the chaotic Chua’s oscillator is extended by a memory element in the form of a double-barrier memristive device. The device consists of [Formula: see text]/Al2O3/Al/Nb layers and exhibits strong analog-type resistive changes depending on the history of the charge flow. In the obtained system we observe strong changes in the dynamics of chaotic oscillations. The otherwise fluctuating amplitudes of Chua’s system are disrupted by transient silent states. Numerical simulations and analysis of the extended model reveal that the underlying dynamics possesses slow–fast properties due to different timescales between the memory element and the base system. Furthermore, the stabilizing and destabilizing dynamic bifurcations are identified that are traversed by the system during its chaotic behavior.

As a result of a hundred million years of evolution, living animals have adapted extremely well to their ecological niche. Such adaptation implies species-specific interactions with their immediate environment by processing sensory cues and responding with appropriate behavior. Understanding how living creatures perform pattern recognition and cognitive tasks is of particular importance for computing architectures: by studying these information pathways refined over eons of evolution, researchers may be able to streamline the process of developing more highly advanced, energy efficient autonomous systems. With the advent of novel electronic and ionic components along with a deeper understanding of information pathways in living species, a plethora of opportunities to develop completely novel information processing avenues are within reach. Here, we describe the basal information pathways in nervous systems, from the local neuron level to the entire nervous system network. The dual importance of local learning rules is addressed, from spike timing dependent plasticity at the neuron level to the interwoven morphological and dynamical mechanisms of the global network. Basal biological principles are highlighted, including phylogenies, ontogenesis, and homeostasis, with particular emphasis on network topology and dynamics. While in machine learning system training is performed on virgin networks without any a priori knowledge, the approach proposed here distinguishes itself unambiguously by employing growth mechanisms as a guideline to design novel computing architectures. Including fundamental biological information pathways that explore the spatiotemporal fundamentals of nervous systems has untapped potential for the development of entirely novel information processing systems. Finally, a benchmark for neuromorphic systems is suggested.