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Register a new userA Laser Spiking Neuron in a Photonic Integrated Circuit
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There has been a recent surge of interest in the implementation of linear operations such as matrix multipications using photonic integrated circuit technology. However, these approaches require an efficient and flexible way to perform nonlinear operations in the photonic domain. We have fabricated an optoelectronic nonlinear device--a laser neuron--that uses excitable laser dynamics to achieve biologically-inspired spiking behavior. We demonstrate functionality with simultaneous excitation, inhibition, and summation across multiple wavelengths. We also demonstrate cascadability and compatibility with a wavelength multiplexing protocol, both essential for larger scale system integration. Laser neurons represent an important class of optoelectronic nonlinear processors that can complement both the enormous bandwidth density and energy efficiency of photonic computing operations.
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We present Simphony, a free and open-source software toolbox for abstracting and simulating photonic integrated circuits, implemented in Python. The toolbox is both fast and easily extensible; plugins can be written to provide compatibility with existing layout tools, and device libraries can be easily created without a deep knowledge of programming. We include several examples of photonic circuit simulations with novel features and demonstrate a speedup of more than 20x over a leading commercially available software tool.
A photonic integrated circuit comprised of an 11 cm multimode speckle waveguide, a 1x32 splitter, and a linear grating coupler array is fabricated and utilized to receive 2 GHz of RF signal bandwidth from 2.5 to 4.5 GHz using a 35 MHz mode locked laser.
We report a compact, scalable, quantum photonic integrated circuit realised by combining multiple, independent InGaAs/GaAs quantum-light-emitting-diodes (QLEDs) with a silicon oxynitride waveguide circuit. Each waveguide joining the circuit can then be excited by a separate, independently electrically contacted QLED. We show that the emission from neighbouring QLEDs can be independently tuned to degeneracy using the Stark Effect and that the resulting photon streams are indistinguishable. This enables on-chip Hong-Ou-Mandel-type interference, as required for many photonic quantum information processing schemes.
A photonic integrated circuit (PIC) comprised of an 11 cm multimode speckle waveguide, a 1x32 splitter, and a linear grating coupler array is fabricated and utilized to receive 2 GHz of radio-frequency (RF) signal bandwidth from 2.5 to 4.5 GHz using compressive sensing (CS). Incoming RF signals are modulated onto chirped optical pulses which are input to the multimode waveguide. The multimode waveguide produces the random projections needed for CS via optical speckle. The time-varying phase and amplitude of two test RF signals between 2.5 and 4.5 GHz are successfully recovered using the standard penalized $l_1$-norm method. The use of a passive PIC serves as an initial step towards the miniaturization of a compressive sensing RF receiver.
Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks, which are encountered in fundamental areas and real-world practical problems including logistics, social networks, and cryptography. Physical machines have recently been proposed and implemented as an alternative to conventional exact and heuristic solvers for the Ising problem, one such optimization task that requires finding the ground state spin configuration of an arbitrary Ising graph. However, these physical approaches usually suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS) capable of converging to the ground state of various 4-spin graphs with high probability. The INPRIS exploits experimental physical noise as a resource to speed up the ground state search. By injecting additional extrinsic noise during the algorithm iterations, the INPRIS explores larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem, and could thus be implemented with optoelectronic architectures that enable GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), our work paves a way for orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.
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