Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs. Integrated optical computing requires programmable photonic and nonlinear elements. The authors demonstrate a phase-change metasurface mode converter, which can be programmed to control the waveguide mode contrast, and build an optical convolutional neural network to perform image processing tasks.

/pdf/programmable-phase-change-metasurfaces-on-waveguides-for-2k5x4demoz.pdf

Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network.

Optical links are moving to higher and higher transmission speeds while shrinking to shorter and shorter ranges where optical links are envisaged even at the chip scale. The scaling in data speed and span of the optical links demands modulators to be concurrently performant and cost-effective. Silicon photonics (SiPh), a photonic integrated circuit technology that leverages the fabrication sophistication of complementary metal-oxide-semiconductor technology, is well-positioned to deliver the performance, price, and manufacturing volume for the high-speed modulators of future optical communication links. SiPh has relied on the plasma dispersion effect, either in injection, depletion, or accumulation mode, to demonstrate efficient high-speed modulators. The high-speed plasma dispersion silicon modulators have been commercially deployed and have demonstrated excellent performance. Recent years have seen a paradigm shift where the integration of various electro-refractive and electro-absorptive materials has opened up additional routes toward performant SiPh modulators. These modulators are in the early years of their development. They promise to extend the performance beyond the limits set by the physical properties of silicon. The focus of our study is to provide a comprehensive review of contemporary (i.e., plasma dispersion modulators) and new modulator implementations that involve the integration of novel materials with SiPh.

/pdf/taking-silicon-photonics-modulators-to-a-higher-performance-28x33hm4hq.pdf

Taking silicon photonics modulators to a higher performance level: state-of-the-art and a review of new technologies

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics, for machine learning algorithms such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on metasurface made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge-Sb-Te during phase transition to control the waveguide spatial modes with a very high precision of up 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate an optical convolutional neural network that can perform image processing and classification tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is very promising toward a large-scale photonic processor for high-throughput optical neural networks.

/pdf/programmable-phase-change-metasurfaces-on-waveguides-for-10mt0zq5tn.pdf

Programmable Phase-change Metasurfaces on Waveguides for Multimode Photonic Convolutional Neural Network

Electro-optic modulators are an indispensable part of photonic communication systems, largely dictating the achievable transmission rate. Recent advances in materials and fabrication/processing techniques have brought new elements and a renewed dynamic to research on optical modulation. Motivated by the new opportunities, this Perspective reviews the state of the art in integrated electro-optic modulators, covering a broad range of contemporary materials and integrated platforms. To provide a better overview of the status of current modulators, an assessment of the different material platforms is conducted on the basis of common performance metrics: extinction ratio, insertion loss, electro-optic bandwidth, driving voltage, and footprint. The main physical phenomena exploited for electro-optic modulation are first introduced, aiming to provide a self-contained reference to researchers in physics and engineering. Additionally, we take care to highlight topics that can be overlooked and require attention, such as the accurate calculation of carrier density distribution and energy consumption, the correct modeling of thin and two-dimensional materials, and the nature of contact electrodes. Finally, a future outlook for the different electro-optic materials is provided, anticipating the research and performance trends in the years to come.

Electro-optic modulation in integrated photonics

pdf/the-physics-of-optical-computing-19jkft1v9g.pdf

The physics of optical computing

Deep learning is one of the most advancing technologies in various fields. Facing the limits of the current electronics platform, optical neural networks (ONNs) based on Si programmable photonic integrated circuits (PICs) have attracted considerable attention as a novel deep learning scheme with optical-domain matrix-vector multiplication (MVM). However, most of the proposed Si programmable PICs for ONNs have several drawbacks such as low scalability, high power consumption, and lack of frameworks for training. To address these issues, we have proposed a microring resonator (MRR) crossbar array as a Si programmable PIC for an ONN. In this article, we present a prototype of a fully integrated 4 ${\rm \times}$ 4 MRR crossbar array and demonstrated a simple MVM and classification task. Moreover, we propose on-chip backpropagation using the transpose matrix operation of the MRR crossbar array, enabling the on-chip training of the ONN. The proposed ONN scheme can establish a scalable, power-efficient deep learning accelerator for applications in both inference and training tasks.

Si microring resonator crossbar array for on-chip inference and training of optical neural network.

We propose Si microring resonator (MRR) crossbar arrays as a programmable nanophotonoic processor (PNP) for a deep learning accelerator. The proposed MRR crossbar array can perform multiply–accumulate (MAC) operation in an optical domain. We numerically reveal that an optical neural network (ONN) based on the proposed MRR crossbar arrays can be used for the pattern recognition task with a similar performance to that of an ONN based on cascaded Mach–Zehnder interferometers (MZIs). We predict that the power consumption can be reduced by approximately tenfold. The small area of the MRR also contributes to the reduction in its chip size by a factor of 36. The fabricated test devices consisting of Si MRRs with phase shifters exhibited no significant crosstalk between neighboring MRRs and showed the feasibility of MAC operation. Photonic integrated circuit (PIC) using the proposed MRR crossbar arrays is promising for large-scale and low-power PNP for deep learning.

Si microring resonator crossbar arrays for deep learning accelerator

A phototransistor is a promising candidate as an optical power monitor in Si photonic circuits since the internal gain of photocurrent enables high responsivity. However, state-of-the-art waveguide-coupled phototransistors suffer from a responsivity of lower than 103 A/W, which is insufficient for detecting very low power light. Here, we present a waveguide-coupled phototransistor operating at a 1.3 μm wavelength, which consists of an InGaAs ultrathin channel on a Si waveguide working as a gate electrode to increase the responsivity. The Si waveguide gate underneath the InGaAs ultrathin channel enables the effective control of transistor current without optical absorption by the gate metal. As a result, our phototransistor achieved the highest responsivity of approximately 106 A/W among the waveguide-coupled phototransistors, allowing us to detect light of 621 fW propagating in the Si waveguide. The high responsivity and the reasonable response time of approximately 100 μs make our phototransistor promising as an effective optical power monitor in Si photonic circuits. 

/pdf/ultrahigh-responsivity-waveguide-coupled-optical-power-3uhuovru.pdf

Ultrahigh-responsivity waveguide-coupled optical power monitor for Si photonic circuits operating at near-infrared wavelengths

We present proof-of-concept taper-less III-V/Si hybrid MOS optical phase shifter. An ultrathin InP membrane enables low insertion loss despite no taper, with keeping high modulation efficiency owing to strong electron confinement at the MOS interface.

Taper-Less III-V/Si Hybrid MOS Optical Phase Shifter using Ultrathin InP Membrane

We demonstrate Si microring resonator crossbar array as a programmable nanophotonic processor. By using multi-wavelength optical signal and monolithically integrated Ge photodetectors, we performed multiply-accumulate operation for optical neural network. We demonstrated classification task for Iris dataset, resulting in prediction accuracy of 91%.

Demonstration of Classification Task Using Optical Neural Network Based on Si Microring Resonator Crossbar Array

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