Modeling of spiking analog neural circuits using organic semiconductor thin film transistors with silicon oxide nitride semiconductor gates

This paper uses the results of the characterization of amorphous semiconductor thin film transistors (TFTs) with the quasi-permanent memory structure referred to as silicon oxide nitride semiconductor (SONOS) gates, to model spiking neural circuits. SONOS gates were fabricated and characterized. In addition, MOSFETs using organic copper phthalocyanine (CuPc) were fabricated with these SONOS gates to demonstrate proof of concept performance. Analog spiking circuits were then modeled using these low performance TFTs to demonstrate the general suitability of organic TFTs in neural circuits. The basic circuit utilizes a standard comparator with charge and discharge circuits. A simple Hebbian learning circuit was added to charge and discharge the SONOS device. The use of these elements allows for the design and fabrication of high-density 3-dimensional circuits that can achieve the interconnect density of biological neural systems.