The electro-optic coefficient (Pockels coefficient) is largest around the absorption resonance of a material. Here, we show that the overall losses, the power consumption and the footprint of plasmonic electro-optic modulators can be reduced when a device is operated in the vicinity of absorption resonances of an electro-optical material. This near-resonant operation in plasmonics is contrary to what is known from photonics where off-resonant operation is required to minimize the overall losses. The findings are supported by experiments demonstrating a reduction in voltage-length product by a factor of 3 and a reduction in loss by a factor 2 when operating a plasmonic modulator near resonance compared to off-resonant.

Memristive-based electro-optical neuromorphic hardware takes advantage of both the high-density of electronic circuits and the high bandwidth of their photonic counterparts, thus showing potential for low-power artificial intelligence applications. In this Perspective paper, we introduce a class of electro-optical memristors that can emulate the key properties of synapses and neurons, which are essential features for the realization of electro-optical neuromorphic functionalities. We then describe the challenges associated with existing technologies and finally give our viewpoint on possible developments toward an energy-efficient neuromorphic platform.

The typically nonlinear and asymmetric response of synaptic memristors to positive and negative electrical pulses makes the realization of accurate deep neural networks very challenging. Here, we integrate a two-terminal valence change memory (VCM) into a photonic/plasmonic circuit and show that the switching properties of this memristor become more gradual and symmetric under light irradiation. The added optical input acts on the VCM as a third, independent modulation channel. It locally heats the active area of the device, which enhances the generation of oxygen vacancies and broadens the resulting nanoscale conductive filaments. The measured conductance modulation of the VCM is then inserted into a neural network simulator. Using the MNIST data set of handwritten digits as an application, a light-enhanced recognition accuracy of 93.53% is demonstrated, similar to ideally performing memristors (94.86%) and much higher than those without light (67.37%). Notably, the optical signal does not increase the overall energy consumption by more than 3.2%. Finally, an approach to scale up our electro-optical technology is proposed, which could allow high-density, energy-efficient neuromorphic computing chips.

A structural change between amorphous and crystalline phase provides a basis for reliable and modular photonic and electronic devices, such as nonvolatile memory, beam steerers, solid-state reflective displays, or mid-IR antennas. In this paper, we leverage the benefits of liquid-based synthesis to access phase-change memory tellurides in the form of colloidally stable quantum dots. We report a library of ternary M x Ge1–x Te colloids (where M is Sn, Bi, Pb, In, Co, Ag) and then showcase the phase, composition, and size tunability for Sn–Ge–Te quantum dots. Full chemical control of Sn–Ge–Te quantum dots permits a systematic study of structural and optical properties of this phase-change nanomaterial. Specifically, we report composition-dependent crystallization temperature for Sn–Ge–Te quantum dots, which is notably higher compared to bulk thin films. This gives the synergistic benefit of tailoring dopant and material dimension to combine the superior aging properties and ultrafast crystallization kinetics of bulk Sn–Ge–Te, while improving memory data retention due to nanoscale size effects. Furthermore, we discover a large reflectivity contrast between amorphous and crystalline Sn–Ge–Te thin films, exceeding 0.7 in the near-IR spectrum region. We utilize these excellent phase-change optical properties of Sn–Ge–Te quantum dots along with liquid-based processability for nonvolatile multicolor images and electro-optical phase-change devices. Our colloidal approach for phase-change applications offers higher customizability of materials, simpler fabrication, and further miniaturization to the sub-10 nm phase-change devices.

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We demonstrate a new concept in an electro-optical memristor where a global light stimulus induces non-volatile conductance changes. The optical signal acts as a third, independent stimulation channel, similar to neuromodulators in three-factor learning rules.

We demonstrate a two times reduction of plasmonic modulator loss by exploiting material resonances of organic electro-optic materials. We measure enhanced in-device electro-optic coefficients with record values of r33=325pm/V reducing UπL threefold.

We integrate memristors in a silicon photonic/plasmonic platform and demonstrate modulators, photodetectors and electronic devices complemented with memory effect. The demonstrated memristors could be the key photonic building blocks in hybrid photonic-electronic neuromorphic chips.