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Conventional computing systems are limited in performance by the well-known von Neumann bottleneck, arising from the physical separation of processor and memory units. The use of electrical signals in such systems also limits computing speeds and introduces significant energy losses. There is thus a pressing need for unconventional computing approaches, ones that can exploit the high bandwidths/speeds and low losses intrinsic to photonics. A promising platform for such a purpose is that offered by integrated phase-change photonics. Here, chalcogenide phase-change materials are incorporated into standard integrated photonics devices to deliver wide-ranging computational functionality, including non-volatile memory and fast, low-energy arithmetic and neuromorphic processing. We report the development of a compact behavioral model for integrated phase-change photonic devices, one which is fast enough to allow system level simulations to be run in a reasonable timescale with basic computing resources, while also being accurate enough to capture the key operating characteristics of real devices. Moreover, our model is readily incorporated with commercially available simulation software for photonic integrated circuits, thereby enabling the design, simulation and optimization of large-scale phase-change photonics systems. We demonstrate such capabilities by exploring the optimization and simulation of the operating characteristics of two important phase-change photonic systems recently reported, namely a spiking neural network system and a matrix-vector photonic crossbar array (photonic tensor core). Results show that use of our behavioral model can significantly facilitate the design and optimization at the system level, as well as expediting exploration of the capabilities of novel phase-change computing architectures. [ABSTRACT FROM AUTHOR]

The combination of phase-change materials and integrated photonics has led to the development of new forms of all-optical devices, including photonic memories, arithmetic and logic processors, and synaptic and neuronal mimics. Such devices can be readily fabricated into photonic integrated circuits, so potentially delivering large-scale all-optical arithmetic-logic units and neuromorphic processing chips. To facilitate in the design and optimization of such large-scale systems, and to aid in the understanding of device and system performance, fast yet accurate computer models are needed. Here, we describe the development of a behavioral modeling tool that meets such requirements, being capable of essentially instantaneous modeling of the write, erase, and readout performance of various integrated phase-change photonic devices, including those for synaptic and neuronal mimics. [ABSTRACT FROM AUTHOR]

The operation of a single class of optical materials in both a volatile and nonvolatile manner is becoming increasingly important in many applications. This is particularly true in the newly emerging field of photonic neuromorphic computing, where it is desirable to have both volatile (short‐term transient) and nonvolatile (long‐term static) memory operation, for instance, to mimic the behavior of biological neurons and synapses. The search for such materials thus far have focused on phase change materials where typically two different types are required for the two different operational regimes. In this paper, a tunable volatile/nonvolatile response is demonstrated in a photonic phase‐change memory cell based on the commonly employed nonvolatile material Ge2Sb2Te5 (GST). A time‐dependent, multiphysics simulation framework is developed to corroborate the experimental results, allowing us to spatially resolve the recrystallization dynamics within the memory cell. It is then demonstrated that this unique approach to photonic memory enables both data storage with tunable volatility and detection of coincident events between two pulse trains on an integrated chip. Finally, improved efficiency and all‐optical routing with controlled volatility are demonstrated in a ring resonator. These crucial results show that volatility is intrinsically tunable in normally nonvolatile GST which can be used in both regimes interchangeably. Observation of optically induced volatility in a traditionally nonvolatile material (Ge2Sb2Te5) is reported on an integrated photonic waveguide. By controlling the optical power in the waveguide, data retention times can be controlled by over six orders of magnitude. This effect is used to detect coincident events in an optical signal and volatility route light, which has applications for optical neuromorphic computing. [ABSTRACT FROM AUTHOR]