Progress in neuromorphic photonics

Abstract

As society's appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

ITO-based electro-absorption modulator for photonic neural activation function

“…Therefore, ITO leads intrinsically to more modulation per unit charge than highquality Silicon. We found in our previous work, that for all materials operating with Pauli blocking (saturable absorption schemes) show comparable values of absorption cross-section (σ ∼ 10 −14 cm 2 at room temperature) 29 , whilst the free carriers offer a worse performance due to non-resonant character of absorption, since according to (8) for free carriers γ e f f = γ + ω 2 /γ > 2ω. To verify these analytical results, obtained from essentially perturbative approach, we calculate the dependence of the absorption coefficient on the injected (induced by the gate) carriers, N 2D as the elemental free carriers responsible for absorption tuning in the electrostatic modulators as…”

“…[1][2][3] The main advantage of photonics over current digital electronics implementations is that distinct signals due to their wave-nature can be straightforwardly and efficiently combined exploiting attojoule efficient electro-optic modulators [4][5][6][7] , phase shifters and combiners, simplifying essential operations such as weighted sum or addition, vector matrix multiplications or convolutions. 8 Moreover, photonics enables high parallelism 9 , hence a higher baud-rate, since multiple wavelengths can travel in the same physical channel by exploiting wavelength-division multiplexing (WDM) 8,[10][11][12][13] . Additionally, photonics is marked by a further degree of freedom in modulating the information carried by the optical waves, since a signal can be modulated by altering its phase, amplitude or polarization.…”

Perspective on photonic memristive neuromorphic computing

“…Some examples are electronic microprocessor with clock rates unable to exceed about four GHz before hitting thermal-dissipation limits and parallel architectures limited to even slower timescales. There are also intrinsic bandwidth limitations and the power density of microelectronic chips no longer stays constant as they get denser, that is, smaller transistors do not consume less power [54]. Current electronic implementations of new information processing architectures, such as neuromorphic architectures are still far from competing with biological neural systems in terms of real-time information-processing capabilities, packing density and energy efficiency.…”

Silicon Photonic Modulator Neuron

“…These properties are favorable for dense, high-bandwidth interconnects [29] in addition to configurable analog signal processing [30][31][32]. Consequently, neuromorphic photonic systems could operate 6-8 ordersof-magnitude faster than neuromorphic electronics [60] with potentially higher energy efficiencies [34]. Optical neural interconnects based on field evolution in freespace [35,36], holograms [37,38], and fiber [39] have been shown but were not widely adopted, in part because they cannot be integrated on a chip and thereby scaled robustly and manufactured cheaply.…”