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Nonvolatile photonic memory cells are basic building blocks for neuromorphic hardware enabling the realization of all‐optical synapses and artificial neurons. These devices commonly exploit chalcogenide phase‐change materials, which are evanescently coupled to photonic waveguides, and provide fast write/erase speeds and large storage capacity. Here, we report for the first time the programming of a nonvolatile photonic memory cell based on Ag3In4Sb76Te17 (AIST) which is capable of mimicking biphasic synapses. We evaluate the underlying mechanism of biphasic behavior of AIST cells based on numerical simulations and correlate to experimental findings. Switching dynamics demonstrate enhanced performance with a post‐excitation dead time as short as 12.8 ns. Based on AIST double cells, we demonstrate reversible multilevel switching between 45 unique synaptic weights for long‐term depression (LTD) and long‐term potentiation (LTP). The observed biphasic programming and excellent switching performance render AIST‐based photonic memory cells promising for artificial neural networks and neuromorphic photonic computing hardware. [ABSTRACT FROM AUTHOR]

All-optical nonvolatile memories enable storage of telecommunication data without detours through electronic circuitry. Phase-change materials provide the means to embed such memories within integrated optical circuits and thus allow combining waveguide devices for information processing with local data storage. Using this concept, we realize an all-photonic memory circuit capable of storing 512 bits of data in an array of nanoscale phase-change devices. We employ multilevel addressing and wavelength multiplexing of microring resonators to write and read a 16 × 16 greyscale image with 2-bit resolution entirely in the optical domain. Our approach holds promise for implementing scalable architectures for on-chip optical data storage with long data retention and ultrafast access times. [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]