Noritsugu Yamamoto

Increasing datacenter demands require power-efficient optical interconnects. However, a conventional standard transmitter using a silicon rib-waveguide Mach-Zehnder modulator and voltage-mode driver has low efficiency and consumes watt-class high power and occupies a several-square-millimeter footprint, which limits large-scale integration for parallel transmission. This paper presents a transmitter consisting of a compact photonic crystal waveguide (PCW) modulator and a current-mode open-collector driver. The PCW modulator is designed to have high impedance in addition to the slow-light effect. The driver connected to the modulator without termination resistors is optimized based on electronics-photonics co-simulations using a standard electronic circuit simulator with an in-house photonic model library. Co-packaging these dramatically reduces the power consumption to 50 mW and a bit energy to 0.78 pJ/bit at 64-Gbaud, and the footprint to 0.66 mm². This result represents a significant advancement toward the integration of a large number of transmission channels with no temperature control.

Integrated photonic neural networks (PNNs) usually adopt traditional convolutional neural network (CNN) or multilayer perceptron (MLP) network models. These models consist of horizontally cascaded deep layer architectures interleaved by nonlinear activation functions. However, there are practical challenges for on-chip realizing such architectures, including the optical loss and the lack of efficient on-chip optical activation nonlinearity. Here, we propose a vertically hierarchical photonic neural network leveraging electro-optical element-wise multiplication to extract an element-wise feature in a polynomial projection space, which enables high-accuracy classification. For this network architecture, the light propagates through only two fully connected linear layers; thus, vertical extension to the deep layer is not limited by optical loss. This electro-photonic network can perform equivalently to or outperform optical CNN and MLP models even without interleaving deep layers by activation functions, benchmarking ∼97.9%, ∼87.7%, and ∼90.3% average blind-testing accuracies, for the whole test sets of MNIST handwritten digits, Fashion-MNIST images, and KMNIST Japanese cursive characters, respectively. It also demonstrates a >99% accuracy for boundary prediction of 12-labeled clusters. This work presents a different PNN architecture, which offers both high performance and better amenability to an integrated photonics platform.

Silicon nitride (SiN) films formed by liquid-source chemical vapor deposition (LSCVD) were analyzed for photonic applications. While this deposition technique for SiN has strengths in its low reaction temperature (>80 °C) and fast deposition rate (>50 nm/min), the material properties, such as its composition, chemical bond, and optical absorption bands in waveguides, have not been studied quantitatively. Hence, we probed SiN films to understand the material characteristics by ultraviolet-to-midinfrared ellipsometry, on-chip waveguide absorption spectrometry, Rutherford backscattering/hydrogen forward-scattering spectrometry, and Fourier-transform infrared spectrometry. As interpreted by a combination of the series of analyses, the N–H bond concentration of higher than 10²¹ cm⁻³ leaves an absorption notch (>6.5 dB/cm) at a wavelength of 1550 nm, which cannot be avoided even by systematically varying the deposition conditions. However, except for that absorption range, a low-loss SiN waveguide (0.5–1.0 dB/cm) with a practically applicable high refractive index (∼1.85) can be formed at the deposition temperature of 100 °C. This basic materials research, which combines multiple spectrometric analyses, will help to improve our understanding of a LSCVD SiN film for photonics circuit integration.