Detecting subthreshold signals in field effect transistors by using stochastic resonance phenomenon

Until recently it is accepted that noise is always a degrading factor for the performance of electronic communication and signal processing systems. For this reason many of the studies on signal processing and communication systems focused on eliminating the negative effects of noise signal on information bearing signal. However, recently it is discovered that noise can play a positive role for the detection of weak signals for electronic systems employing nonlinear electronic devices. It is seen that the detection of the weak signals in nonlinear systems is possible when an optimum amount of noise is added to the information bearing signal before passing the signal through the nonlinear system. And this concept is named as stochastic resonance, i.e., SR. In this thesis it is shown that it is possible to develop a training based model for the SR systems that decides optimum noise signal to be added to the weak information signal directly for the best system performance. In the second part of the thesis study a practical application of the SR concept on a nonlinear electronic device, field effect transistor (FET), is demonstrated. For this purpose a user interface program using C# is developed and some part of the signal processing is done using the field programmable gate arrays (FPGA).