A New Family of Ultralow Loss Reversible Phase‐Change Materials for Photonic Integrated Circuits: Sb ₂ S ₃ and Sb ₂ Se ₃

The identification of materials suitable for non-volatile phase-change memory applications is driven by the need to find materials with tailored properties for different technological applications and the desire to understand the scientific basis for their unique properties. Here, we report the observation of a distinctive and characteristic feature of phase-change materials. Measurements of the dielectric function in the energy range from 0.025 to 3 eV reveal that the optical dielectric constant is 70-200% larger for the crystalline than the amorphous phases. This difference is attributed to a significant change in bonding between the two phases. The optical dielectric constant of the amorphous phases is that expected of a covalent semiconductor, whereas that of the crystalline phases is strongly enhanced by resonant bonding effects. The quantification of these is enabled by measurements of the electronic polarizability. As this bonding in the crystalline state is a unique fingerprint for phase-change materials, a simple scheme to identify and characterize potential phase-change materials emerges.

Software-implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses which, when connected in networks or neuromorphic systems, process information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and bandwidth inherent to optical systems, attractive for the direct processing of optical telecommunication and visual data.

Materials whose optical properties can be reconfigured are crucial for photonic applications such as optical memories. Phase-change materials offer such utility and here recent progress is reviewed.