Abstract
This paper uses the results of the characterization of amorphous semiconductor thin film transistors (TFTs) with a split gate and the quasi-permanent memory structure referred to as silicon oxide nitride semiconductor (SONOS) gates, to model spiking neural circuits with Hebbian learning ability. MOSFETs using organic (tris 8-hydroxyquinolinate aluminum (Alq3), copper phthalocyanine (CuPc)) and inorganic (ZnO) amorphous materials can be fabricated with split gates, which will provide multiple synaptic inputs. A simple Hebbian learning circuit is added to charge and discharge the SONOS device. The primary result of this work is the demonstration of the practicality of using SONOS amorphous organic TFTs with multiple gates and imbedded Hebbian learning capability in spiking neuron analog circuits. 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.
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Wood, R., Bruce, I., Mascher, P. (2012). Modeling of Spiking Analog Neural Circuits with Hebbian Learning, Using Amorphous Semiconductor Thin Film Transistors with Silicon Oxide Nitride Semiconductor Split Gates. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_12
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DOI: https://doi.org/10.1007/978-3-642-33269-2_12
Publisher Name: Springer, Berlin, Heidelberg
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