Organic phototransistors using poly(3-hexylthiophene) nanofibres.

Here we report the fabrication of nanofibre-based organic phototransistors (OPTs) using preformed poly(3-hexylthiophene) (P3HT) nanofibres. OPT performance is analysed based on two important parameters: photoresponsivity R and photosensitivity P. Before testing the devices as OPTs, the normal organic field-effect transistor (OFET) operation is characterized, revealing a surface-coverage-dependent performance. With R reaching 250 A W(-1) in the on-state (V(GS) = -40 V) and P reaching 6.8 × 10(3) in the off-state (V(GS) = 10 V) under white light illumination (I(inc) = 0.91 mW cm(-2)), the best nanofibre-based OPTs outperform the OPTs fabricated from a solution of P3HT in chlorobenzene, in which no preformed fibres are present. The better performance is attributed to an increase in active layer crystallinity, a better layer connectivity and an improved edge-on orientation of the thiophene rings along the polymer backbone, resulting in a longer exciton diffusion length and enhanced charge carrier mobility, linked to a decreased interchain coupling energy. In addition, the increased order in the active layer crystallinity induces a better spectral overlap between the white light emission spectrum and the active layer absorption spectrum, and the absorption of incident light is maximised by the favourable parallel orientation of the polymer chains with respect to the OPT substrate. Combining both leads to an increase in the overall light absorption. In comparison with previously reported solution-processed organic OPTs, it is shown here that no special dielectric surface treatment or post-deposition treatment of the active device layer is needed to obtain high OPT performance. Finally, it is also shown that, inherent to an intrinsic gate-tuneable gain mechanism, changing the gate potential results in a variation of R over at least five orders of magnitude. As such, it is shown that R can be adjusted according to the incident light intensity.

Toward optical signal processing using photonic reservoir computing.

We propose photonic reservoir computing as a new approach to optical signal processing in the context of large scale pattern recognition problems. Photonic reservoir computing is a photonic implementation of the recently proposed reservoir computing concept, where the dynamics of a network of nonlinear elements are exploited to perform general signal processing tasks. In our proposed photonic implementation, we employ a network of coupled Semiconductor Optical Amplifiers (SOA) as the basic building blocks for the reservoir. Although they differ in many key respects from traditional software-based hyperbolic tangent reservoirs, we show using simulations that such a photonic reservoir can outperform traditional reservoirs on a benchmark classification task. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed.