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The photonics platform has been considered increasingly promising for neuromorphic computing, due to its potential in providing low latency and energy efficient large-scale parallel connectivity. Phase change materials (PCMs) have been recently employed to introduce all-optical non-volatile memory in integrated photonic circuits, especially finding application as non-volatile weighting element in photonic artificial neural networks. Interestingly, these weighting elements can potentially be used as building blocks for large-scale networks that can autonomously adapt to their input, i.e. presenting the property of plasticity, similarly to the biological brain. In this work, we develop a computationally efficient dynamical model of a silicon ring resonator (RR) enhanced by a phase change material, namely Ge2Sb2Te5 (GST). We do so starting from two existing dynamical models (of a silicon RR and of a GST thin film on a straight silicon waveguide), but extending the optical equations to properly account for the high absorption and asymmetry in the ring due to the phase change material. Our model accounts for silicon nonlinear effects due to free carriers and temperature, as well as for the phase change of GST, whose energy efficiency and optical contrast can be enhanced by the RR resonant behaviour. We also restructure the optical equations so that the model can be efficiently employed in a modular way within a commercial software for system-level photonics simulations. Moreover, exploiting the developed model, we explore several design parameters and show that both speed and energy efficiency of memory operations can be enhanced by factors from six to ten. Also, we show that the achievable optical contrast due to GST phase change can be increased by more than a factor ten by leveraging the resonant properties of the RR, at the expense of higher optical loss. Finally, by exploiting the nonlinear dynamics arising in silicon RR networks, we show that a strong contrast is achievable while preserving energy efficiency.

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge 2 Sb 2 Te 5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.

Perfect absorber type devices are well-suited to many applications, such as solar cells, spatial light modulators, bio-sensors, and highly-sensitive photo-detectors. In such applications, a method for the design and fabrication of devices in a simple and efficient way, while at the same time maintaining design control over the key performance characteristics of resonant frequency, reflection coefficient at resonance and quality factor, would be particularly advantageous. In this work we develop such a method, based on eigenmode analysis and critical coupling theory, and apply it to the design of reconfigurable phase-change metasurface absorber devices. To validate the method, the design and fabrication of a family of absorbers was carried out with a range of 'on-demand' quality factors, all operating at the same resonant frequency and able to be fabricated simply and simultaneously on the same chip. Furthermore, by switching the phase-change layer between its amorphous and crystalline states, we show that our devices can provide an active or reconfigurable functionality.

Phase-change chalcogenide alloys, such as Ge