Asymmetric excitable phase triggering in an optically injected semiconductor laser

Real-time signal acquisition based on optical excitable response by pre-designing single-tone preamble waveform

Real-time acquisition of target signals is preferred for mobile communication systems. However, under the requirement of ultra-low latency for next-generation communication, traditional acquisition methods need to temporally locate the target signal from a large amount of raw data with correlation-based computing, introducing extra latency. We propose a real-time signal acquisition method based on an optical excitable response (OER) by pre-designing a single-tone preamble waveform. The preamble waveform is designed to be within the amplitude and bandwidth of the target signal, so no extra transceiver is required. The OER generates a corresponding pulse to the preamble waveform in the analog domain, which simultaneously triggers an analog-to-digital converter (ADC) to acquire target signals. The dependence of OER pulse on the preamble waveform parameter is studied, leading to a pre-design of the preamble waveform for an optimal OER. In the experiment, we demonstrate a millimeter-wave (26.5-GHz) transceiver system with target signals of orthogonal frequency division multiplexing (OFDM) format. Experimental results show that the response time is less than 4 ns, which is far lower than the ms-level response time of traditional all-digital time-synchronous acquisition methods.

Refractory times for excitable dual-state quantum dot laser neurons

Excitable photonic systems show promise for ultrafast analog computation, several orders of magnitude faster than biological neurons. Optically injected quantum dot lasers display several excitable mechanisms with dual-state quantum lasers recently emerging as true all-or-none excitable artificial neurons. For use in applications, deterministic triggering is necessary and this has previously been demonstrated in the literature. In this work we analyze the crucially important refractory time for this dual-state system, which defines the minimum time between distinct pulses in any train.

Inhibitory and excitatory integration with a quantum dot laser neuron

Neuromorphic computing has garnered a lot of attention in recent years. Excitable photonic systems in particular demonstrate great potential for ultrafast, controllable spike processing. Optically injected quantum dot lasers display several distinct excitable regimes. We demonstrate here that optically injected dual-state quantum dot lasers can display the classic leaky integrate-and-fire mechanism where the integration of several sub-threshold perturbations can yield an effective supra-threshold perturbation. Intriguingly, a contrasting integrate-and-inhibit mechanism is demonstrated in this work where the integration of two supra-threshold perturbations yields an effective sub-threshold perturbation similar to the pre-pulse inhibition mechanism of biological neurons. This is the first such mechanism in neuromorphic photonics to the best of our knowledge.

Analog-to-spike encoding and time-efficient RF signal processing with photonic neurons

The radio-frequency (RF) signal processing in real time is indispensable for advanced information systems, such as radar and communications. However, the latency performance of conventional processing paradigm is worsened by high-speed analog-to-digital conversion (ADC) generating massive data, and computation-intensive digital processing. Here, we propose to encode and process RF signals harnessing photonic spiking response in fully-analog domain. The dependence of photonic analog-to-spike encoding on threshold level and time constant is theoretically and experimentally investigated. For two classes of waveforms from real RF devices, the photonic spiking neuron exhibits distinct distributions of encoded spike numbers. In a waveform classification task, the photonic-spiking-based scheme achieves an accuracy of 92%, comparable to the K-nearest neighbor (KNN) digital algorithm for 94%, and the processing latency is reduced approximately from 0.7 s (code running time on a CPU platform) to 80 ns (light transmission delay) by more than one million times. It is anticipated that the asynchronous-encoding, and binary-output nature of photonic spiking response could pave the way to real-time RF signal processing.

Ultrafast neuromorphic photonic image processing with a VCSEL neuron

The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100 ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.

Optical information processing using dual state quantum dot lasers: complexity through simplicity

We review results on the optical injection of dual state InAs quantum dot-based semiconductor lasers. The two states in question are the so-called ground state and first excited state of the laser. This ability to lase from two different energy states is unique amongst semiconductor lasers and in combination with the high, intrinsic relaxation oscillation damping of the material and the novel, inherent cascade like carrier relaxation process, endows optically injected dual state quantum dot lasers with many unique dynamical properties. Particular attention is paid to fast state switching, antiphase excitability, novel information processing techniques and optothermally induced neuronal phenomena. We compare and contrast some of the physical properties of the system with other optically injected two state devices such as vertical cavity surface emitting lasers and ring lasers. Finally, we offer an outlook on the use of quantum dot material in photonic integrated circuits.