This work is based on photosensitive carbon nanotube transistors called OG-CNTFETs (optically gated carbon nanotube FETs) that we studied in details both as photo-detectors and as memory devices [2,3]. We showed that such devices can be used as a resistive memory elements  and proposed ways to implement them to store synaptic weights in neural network circuits [5,6]. The propose implementation is ideally suited for the parallel learning of multiple functions in a crossbar array of OG-CNTFETs as shown by simulations [5,6].
At the experimental level, using non-scaled down devices, we also showed that the same ensemble of 8 devices can be trained multiple times to code successively any 3-input linearly separable Boolean logic function despite device-to-device variability. This work represents one of the very few demonstrations of actual function learning with synapses based on nano-scale building blocks.  The potential of such approach for the parallel learning of multiple and more complex functions will be discussed. Finally we explore the scalability of such strategy by evaluating programming speed  and size issues  in single-nanotube based devices.
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Fig. 1 (top) optical microscope image of a line of OG-CNTFETs based on carbon nanotube networks and SEM image of one device. (middle) principal of the learning circuit with differential inputs and conventional electronics as neurone. (bottom) example of 3 partiucamlr 3-input functions learned successively by the same series of CNT-devices. Extracted from .
Such use of 3-terminal devices as resistive memory elements, while powerful from a function learning perspective, also has some limitations in terms of ultimate scaling. We thus also study 2-terminal memristors incorporating carbon nanotubes, not as switching medium, but as nano-scale electrodes .
This work is supported by the ANR (Panini ANR-07-ARFU-008 and Moorea ANR-12-BS03-004 Projects), the C’NANO IdF (Cinamon Project) and the EU (Nabab Project FP7-216777).
 Cabaret et al., submitted.