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.
State-of-the-art SONOS (silicon-oxide-nitride-oxide-polysilicon) field effect transistors were operated in a memristive switching mode. The circuit design is a variation of the MemFlash concept and the particular properties of depletion type SONOS-transistors were taken into account. The transistor was externally wired with a resistively shunted pn-diode. Experimental current-voltage curves show analog bipolar switching characteristics within a bias voltage range of ±10 V, exhibiting a pronounced asymmetric hysteresis loop. The experimental data are confirmed by SPICE simulations. The underlying memristive mechanism is purely electronic, which eliminates an initial forming step of the as-fabricated cells. This fact, together with reasonable design flexibility, in particular to adjust the maximum R ON /R OFF ratio, makes these cells attractive for neuromorphic applications. The relative large set and reset voltage around ±10 V might be decreased by using thinner gate-oxides. The all-electric operation principle, in combination with an established silicon manufacturing process of SONOS devices at the Semiconductor Foundry X-FAB, promise reliable operation, low parameter spread and high integration density.
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.
The influences of varying charge storage capabilities of EEPROM transistors in a MemFlash configuration are presented. The effect a thinning of the tunnelling oxide has on the retention and the corresponding hysteretic current–voltage curves of MemFlash cells is investigated through measurements as well as simulations. Furthermore, the variation of charging and discharging voltages along with different cycle frequencies is explored in respect to the change in hysteretic behavior. Finally, the influences of changing device parameters on the behavior as artificial synapses were investigated by emulating LTP for different MemFlash cells. Here, we found a strong dependency of the learning rate and the memory capabilities from the tunnel oxide thickness, which allows flexible application in neuromorphic computing schemes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.