Self-assembled monolayers of long chain alkanes deposited on silicon wafers using an optimally designed procedure exhibit very large energy barriers ( $\sim 4.5$ eV) to carrier tunneling. The dc conductivity is found to be $\sim 4.6\times {10}^{-15\mathrm{}}$ S/cm, close to that of bulk polyethylene, and independent of monolayer thickness. This demonstrates that the tunneling contribution to the overall conductivity can be made negligible when the organic structure is densely packed and highly ordered. Distinctly higher conductivity values are observed with disordered monolayers.

We made nanometer-scale (gate length of 30 nm) organic thin-film transistors using a self-assembled monolayer (2 nm thick) as a gate insulator. The fabrication steps combine electron-beam lithography and lift-off techniques for the deposition of both metal electrodes and organic semiconductors with a chemical approach (self-assembly of organic molecules) to fabricate the gate insulator. Good performances of these transistors (with a record subthreshold slop of 350 mV/decade and a cutoff frequency of 20 kHz) and low-voltage operation (<2 V) are demonstrated down to a gate length of 200 nm. A gate voltage modulation of the source-to-drain tunnel current is demonstrated for the 30 nm gate length device.

Memristive devices present a new device technology allowing for the realization of compact nonvolatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.