Stochastic resonance (SR) in carbon nanotube field-effect transistors (CNT-FETs) was investigated to enhance their weak-signal response. When weak pulse trains were applied to the gate of a CNT-FET operating in a subthreshold region, the correlation between the input and output voltages increased upon addition of noise with optimized intensity. Virtual CNT-FET summing networks of N units were demonstrated to improve SR. When no noise was applied for N=1, the correlation coefficient was nearly 0. While, the correlation coefficient at the peak intensity for N=8 was estimated to be 0.58, indicating that significant enhancement of the correlation was observed in the summing network of the CNT-FETs. Moreover, as N increased, the larger correlation coefficient was obtained against large noise intensity, indicating that they are robust against a large amount of unintentional noise. Therefore, CNT-FET summing networks based on SR are promising candidates for highly sensitive label-free sensors.
A solution-gated carbon nanotube field-effect transistor (CNTFET) based on stochastic resonance (SR) was investigated in order to enhance small-signal detection under ambient noise conditions. When noise of optimal intensity was introduced at the reference electrode in a nonlinear CNTFET, the electric double layer in the solution was modulated, resulting in SR behavior. Moreover, when the CNTFET was used as a pH sensor, high sensitivity was achieved, which enabled the detection of small differences in pH. The best results were obtained in a noisy environment; therefore, a solution-gated SR-based CNTFET operated in the subthreshold regime is a promising high-sensitivity sensor.
Robust noise characteristics in carbon nanotube field-effect transistors (CNT-FETs) based on stochastic resonance (SR) were demonstrated to detect small signals in noisy environments. When weak pulse trains were applied to a CNT-FET in the subthreshold regime, the correlation coefficient between the input and output signals increased upon adding an appropriate intensity of noise. Offset-voltage dependences were investigated, and moreover, a virtual summing network was formed using CNT-FETs having different offset voltages. The measurement indicated that responses correlated with the input signals were enhanced in a wide range of noise intensity. Therefore, the summing network based on SR is a promising candidate for highly sensitive label-free sensors which are to be utilized in unintentionally noisy environments.
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