arXiv subjects

Marcus Tamura

Publications and source records attributed to Marcus Tamura.

5 recordsLinked to original sources

arXiv2025

Photoluminescent colour centres on a mainstream silicon photonic foundry platform

The fabrication of silicon photonic components in commercial CMOS-compatible foundries has revolutionized the impact of silicon photonics on advancing communication, quantum computing and artificial intelligence, due to their benefits of mass production, high throughput, low cost, and high performance. The indirect bandgap of silicon introduces a fundamental challenge; thus, the mainstream silicon-on-insulator (SOI) platform does not have efficient light sources. Recently, luminescent colour centres in SOI have emerged as one promising approach for developing efficient on-chip classical and quantum light sources, although past work has relied on custom fabrication that is not foundry-compatible. In this work, we demonstrate W-centre photoluminescence on a mainstream silicon photonics platform through development of a straightforward back end-of-line (BEOL) treatment. At an optimal implant energy of 7~MeV, we observed W-centre photoluminescence with a brightness comparable to prior in-house processes. We performed a series of experiments on Circular Bragg Grating (CBG) devices with varying pitches, duty cycles, and implant energies to confirm the PL emission from the encapsulated SOI device layer rather than the handle wafer. Our novel approach in fabricating silicon colour centres in commercial silicon photonic foundry processes opens up new opportunities for integrating classical and quantum light sources directly onto silicon photonic circuits, unlocking opportunities for large-scale integration of advanced photonic architectures on chip.

physics.optics↗

arXiv2024

High-Speed Multifunctional Photonic Memory on a Foundry-Processed Photonic Platform

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result showcases a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments.

physics.optics↗

arXiv2024

Nonlinear dynamics in neuromorphic photonic networks: physical simulation in Verilog-A

Advances in silicon photonics technology have enabled the field of neuromorphic photonics, where analog neuron-like processing elements are implemented in silicon photonics technology. Accurate and scalable simulation tools for photonic integrated circuits are critical for designing neuromorphic photonic circuits. This is especially important when designing networks with recurrent connections, where the dynamics of the system may give rise to unstable and oscillatory solutions which need to be accurately modelled. These tools must simultaneously simulate the analog electronics and the multi-channel (wavelength-division-multiplexed) photonics contained in a photonic neuron to accurately predict on-chip behaviour. In this paper, we utilize a Verilog-A model of the photonic neural network to investigate the dynamics of recurrent integrated circuits. We begin by reviewing the theory of continuous-time recurrent neural networks as dynamical systems and the relation of these dynamics to important physical features of photonic neurons such as cascadability. We then present the neural dynamics of systems of one and two neurons in the simulated Verilog-A circuit, which are compared to the expected dynamics of the abstract CTRNN model. Due to the presence of parasitic circuit elements in the Verilog-A simulation, it is seen that there is a topological equivalence, but not an exact isomorphism, between the theoretical model and the simulated model. The implications of these discrepancies for the design of neuromorphic photonic circuits are discussed. Our findings pave the way for the practical implementation of large-scale silicon photonic recurrent neural networks.

cs.ET↗

arXiv2024

Physics to System-level Modeling of Silicon-organic-hybrid Nanophotonic Devices

The continuous growth in data volume has sparked interest in silicon-organic-hybrid nanophotonic devices integrated into silicon photonic integrated circuits (PICs). SOH devices offer improved speed and energy efficiency compared to silicon photonics devices. However, a comprehensive and accurate modeling methodology of SOH devices is lacking. While some preliminary modeling approaches for SOH devices exist, their reliance on theoretical and numerical methodologies, along with a lack of compatibility with electronic design automation (EDA), hinders their seamless and rapid integration with silicon PICs. Here, we develop a phenomenological, building-block-based SOH PICs simulation methodology spanning from the physics to the system level, offering high accuracy, comprehensiveness, and EDA-style compatibility. Our model is also readily integrable and scalable, lending itself to the design of large-scale silicon PICs. Our proposed modeling methodology is agnostic and compatible with any photonics-electronics co-simulation software. We validate this methodology by comparing experimentally demonstrated SOH microring modulators and Mach Zehnder modulators with those obtained through simulation, demonstrating its ability to model various modulator topologies. We also show our methodology's ease and speed in modeling large-scale systems. As an illustrative example, we use our methodology to design and study a 3-channel SOH MRM-based wavelength-division (de)multiplexer, a widely used component in various applications, including neuromorphic computing, data center interconnects, communications, and sensing. Our modeling approach is compatible with other materials exhibiting the Pockels and Kerr effects. To our knowledge, this represents the first comprehensive physics-to-system-level EDA-compatible simulation methodology for SOH modulators.

physics.optics↗

arXiv2019

Deep Learning-Guided Surface Characterization for Autonomous Hydrogen Lithography

As the development of atom scale devices transitions from novel, proof-of-concept demonstrations to state-of-the-art commercial applications, automated assembly of such devices must be implemented. Here we present an automation method for the identification of defects prior to atomic fabrication via hydrogen lithography using deep learning. We trained a convolutional neural network to locate and differentiate between surface features of the technologically relevant hydrogen-terminated silicon surface imaged using a scanning tunneling microscope. Once the positions and types of surface features are determined, the predefined atomic structures are patterned in a defect-free area. By training the network to differentiate between common defects we are able to avoid charged defects as well as edges of the patterning terraces. Augmentation with previously developed autonomous tip shaping and patterning modules allows for atomic scale lithography with minimal user intervention.

cond-mat.mtrl-sci↗