Abstract
With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.
Highlights
We are currently witnessing an exponential growth of artificial intelligence systems to help humans dealing with highly complex tasks, such as sensing and learning,[1,2,3,4,5] needed for the internet of things and to handle with big data
We present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes
The carrier flow through a double barrier quantum well resonant tunneling diode (DBQW-RTD) is fundamentally different from that of a single barrier because the DBQW structure acts as filter to charge carrier energy distribution by controlling the number of carriers that can take part in the conduction through the resonant levels
Summary
We are currently witnessing an exponential growth of artificial intelligence systems to help humans dealing with highly complex tasks, such as sensing and learning,[1,2,3,4,5] needed for the internet of things and to handle with big data. One of the most promising alternatives is to use light-based synapses enabled by neuromorphic photonic integrated chips This approach takes advantage of energy efficient optical interconnects to achieve low-power neuron-like responses at speeds one billion times faster than neurons (>1 Gb/s). We address the neural spike dynamic characteristics detailed above in the excitable dynamic regime (point 3) combined with a mechanism of time-delayed feedback The neural spike response in the autaptic configuration displays all the signatures of spatio-temporal localized structures (LSs) of light (see a recent review in Ref. 55) As discussed in Sec. IV E, by taking advantage of the dissipative nature of the LSs, robust and flexible data reconfiguration and clock and timing can be achieved in our neuromorphic chips at high-speeds and with short transients
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