Phase change materials are increasingly becoming important functional materials for applications in emerging integrated optics. Since the demonstration of a photonic phase change memory device in 2015, several new applications i this area have emerged ranging from lossless routing to on-chip photonics synapses. More recently the use of these materials in unconventional computing has seen an emerging interest, especially in the areas of optical abacuses and other forms of brain-inspired computing. There have also been advances in non-von Neumann approaches to carry out large-scale matrix multiplications. In this talk, I shall cover these topics and present a future view of these materials, not only in computation, but also in displays and holographic projections.
Black phosphorus stands out from the family of two-dimensional materials as a semiconductor with a direct, layer-dependent bandgap in energy corresponding to the spectral range from the visible to the mid-infrared (mid-IR), as well as many other attractive optoelectronic attributes. It is, therefore, a very promising material for various optoelectronic applications, particularly in the important mid-IR range. While mid-IR technology has been advancing rapidly, both photodetection and electro-optic modulation in the mid-IR rely on narrow-band compound semiconductors, which are difficult and expensive to integrate with the ubiquitous silicon photonics. For mid-IR photodetection, black phosphorus has been proven to be a viable alternative. Here, we demonstrate electro-optic modulation of mid-IR absorption in few-layer black phosphorus under field applied by an electrostatic gate. Our experimental and theoretical results find that, within the doping range obtainable in our samples, the quantum confined Franz-Keldysh effect is the dominant mechanism of electro-optic modulation. Spectroscopic study on samples with varying thickness reveals strong layer-dependence in the inter-band transition between different sub-bands. Our results show black phosphorus is a very promising material to realizing efficient mid-IR modulators.
The use of photonics in computing is a hot topic of interest, driven by the need for ever-increasing speed along with reduced power consumption. In existing computing architectures, photonic data storage would dramatically improve the performance by reducing latencies associated with electrical memories. At the same time, the rise of ‘big data’ and ‘deep learning’ is driving the quest for non-von Neumann and brain-inspired computing paradigms. To succeed in both aspects, we have demonstrated non-volatile multi-level photonic memory avoiding the von Neumann bottleneck in the existing computing paradigm and a photonic synapse resembling the biological synapses for brain-inspired computing using phase-change materials (Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>).