In this study, the possibilities of noise tailoring in filamentary resistive switching
memory devices are investigated. To this end, the resistance and frequency scaling of
the low-frequency 1/
f
-type noise properties are studied in
representative mainstream material systems. It is shown that the overall noise floor is
tailorable by the proper material choice, as demonstrated by the order-of-magnitude
smaller noise levels in Ta
2
O
5
and Nb
2
O
5
transition-metal oxide memristors compared to Ag-based devices. Furthermore, the
variation of the resistance states allows orders-of-magnitude tuning of the relative
noise level in all of these material systems. This behavior is analyzed in the framework
of a point-contact noise model highlighting the possibility for the disorder-induced
suppression of the noise contribution arising from remote fluctuators. These findings
promote the design of multipurpose resistive switching units, which can simultaneously
serve as analog-tunable memory elements and tunable noise sources in probabilistic
computing machines.
Nowadays gas detection in the ppm and sub-ppm domain is essential in terms of environmental protection as well as reducing sanitary risks. However, detecting systems to perform these measurements (e.g., gas chromatographs) are expensive and take up too much space, thus their use is not likely to become wide-spread. Small, cheap and easily mountable sensors, such as resistive sensors are more applicable for this purpose. But the main disadvantage of these sensors is the lack of chemical selectivity. Yet, a novel method called fluctuation-enhanced sensing (FES), which considers the sensor noise as the source of chemical information, can be used to improve selectivity. Since carbon nanotube (CNT)-based sensors are regarded as promising devices for FES measurements, we investigated whether stationary fluctuations in output signal (dc-resistance) of a CNT sensor could be used to increase chemical selectivity. In this work we prove that FES is applicable to increase selectivity of CNT sensors: air polluting gases ( N2O , NH 3 and H2S ) and their mixtures can be distinguished. Furthermore, we also show that different concentrations of the same analyte can be differentiated and chemical selectivity can be extended into the sub-ppm region.
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