We present GDSFactory, an open-source Python library for integrated circuit design automation supporting photonics, analog, quantum, and MEMS applications. The platform provides unified workflows for full chip development: layout design and simulations (device and circuit via S-parameter analysis).

In neuromorphic photonic systems, device operations are typically governed by analog signals, necessitating digital-to-analog converters (DAC) and analog-to-digital converters (ADC). However, data movement between memory and these converters in conventional von Neumann architectures incur significant energy costs. We propose an analog electronic memory co-located with photonic computing units to eliminate repeated long-distance data movement. Here, we demonstrate a monolithically integrated neuromorphic photonic circuit with on-chip capacitive analog memory and evaluate its performance in machine learning for in situ training and inference using the MNIST dataset. Our analysis shows that integrating analog memory into a neuromorphic photonic architecture can achieve over 26 × power savings compared to conventional SRAM-DAC architectures. Furthermore, maintaining a minimum analog memory retention-to-network-latency ratio of 100 maintains  >90% inference accuracy, enabling leaky analog memories without substantial performance degradation. This approach reduces reliance on DACs, minimizes data movement, and offers a scalable pathway toward energy-efficient, high-speed neuromorphic photonic computing.

Neuromorphic photonics has become one of the research forefronts in photonics, with its benefits in low-latency signal processing and potential in significant energy consumption reduction when compared with digital electronics. With artificial intelligence (AI) computing accelerators in high demand, one of the high-impact research goals is to build scalable neuromorphic photonic integrated circuits which can accelerate the computing of AI models at high energy efficiency. A complete neuromorphic photonic computing system comprises seven stacks: materials, devices, circuits, microarchitecture, system architecture, algorithms, and applications. Here, we consider microring resonator (MRR)-based network designs toward building scalable silicon integrated photonic neural networks (PNN), and variations of MRR resonance wavelength from the fabrication process and their impact on PNN scalability. Further, post-fabrication processing using organic photochromic layers over the silicon platform is shown to be effective for trimming MRR resonance wavelength variation, which can significantly reduce energy consumption from the MRR-based PNN configuration. Post-fabrication processing with photochromic materials to compensate for the variation in MRR fabrication will allow a scalable silicon system on a chip without sacrificing today's performance metrics, which will be critical for the commercial viability and volume production of large-scale silicon photonic circuits.

Silicon photonic neural networks can achieve higher throughputs and lower latencies than digital electronic alternatives. However, recently reported implementations of such networks have lacked integrated signal gain, instead utilizing off-chip amplifiers or co-processors to complete the signal processing pipeline. Photonic neural networks without gain face substantial limitations in network depth and inter-layer fan-out. Here, we demonstrate a fully integrated silicon photonic modulator neuron capable of up to 14.1 dB gain, achieved by modeling and addressing self-heating behavior in our output PN-junction micro-ring modulator. We use our experimental neuron to emulate a small network subject to high loss, achieving superior accuracy on an automated modulation classification benchmark to that of an optimal linear system. Our high-gain neuron can serve as a building block vastly expanding the range of neural network architectures that can be implemented with silicon photonics. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

Neuromorphic (brain-inspired) photonics accelerates AI1 with high-speed, energy-efficient solutions for RF communication2, image processing3,4, and fast matrix multiplication5,6. However, integrated neuromorphic photonic hardware faces size constraints that limit network complexity. Recent advances in photonic quantum hardware7 and performant trainable quantum circuits8 offer a path to more scalable photonic neural networks. Here, we show that a combination of classical network layers with trainable continuous variable quantum circuits yields hybrid networks with improved trainability and accuracy. On a classification task, these hybrid networks match the performance of classical networks nearly twice their size. These performance benefits remain even when evaluated at state-of-the-art bit precisions for classical and quantum hardware. Finally, we outline available hardware and a roadmap to hybrid architectures. These hybrid quantum-classical networks demonstrate a unique route to enhance the computational capacity of integrated photonic neural networks without increasing the network size.

In the past decade, the field of neuromorphic photonics has experienced significant growth. To extend the reach of this technology, researchers continue to push the limits of these systems with respect to network size and bandwidth. However, without proper RF-optimized architectural designs, as operating frequencies are scaled up, significant losses of RF power can be incurred at each neuron. Within the broadcast and weight neuromorphic photonic architecture, this excess loss will be accumulated until processing is no longer feasible. If designed properly, RF loss can be minimized significantly, and residual loss could be compensated by coin-tegrated transimpedance amplifiers, thus enabling further scaling of the network. In this paper, the authors present broadband weighting of RF input signals with a 3-dB bandwidth of 4.28 GHz, utilizing the linear front-end of a silicon photonic neural network. Additionally, the authors present link loss measurements and analysis.

Micro-ring resonators can tunably weight wavelength-division-multiplexed signals, but they face high sensitivity to temperature. In this work, we take advantage of micro-ring self-heating to experimentally stabilize a silicon micro-ring weight, nearly halving its thermal sensitivity.

Radio-frequency interference is a growing concern as wireless technology advances, with potentially life-threatening consequences like interference between radar altimeters and 5G cellular networks. Mobile transceivers mix signals with varying ratios over time, posing challenges for conventional digital signal processing (DSP) due to its high latency. These challenges will worsen as future wireless technologies adopt higher carrier frequencies and data rates. However, conventional DSPs, already on the brink of their clock frequency limit, are expected to offer only marginal speed advancements. This paper introduces a photonic processor to address dynamic interference through blind source separation (BSS). Our system-on-chip processor employs a fully integrated photonic signal pathway in the analogue domain, enabling rapid demixing of received mixtures and recovering the signal-of-interest in under 15 picoseconds. This reduction in latency surpasses electronic counterparts by more than three orders of magnitude. To complement the photonic processor, electronic peripherals based on field-programmable gate array (FPGA) assess the effectiveness of demixing and continuously update demixing weights at a rate of up to 305 Hz. This compact setup features precise dithering weight control, impedance-controlled circuit board and optical fibre packaging, suitable for handheld and mobile scenarios. We experimentally demonstrate the processor's ability to suppress transmission errors and maintain signal-to-noise ratios in two scenarios, radar altimeters and mobile communications. This work pioneers the real-time adaptability of integrated silicon photonics, enabling online learning and weight adjustments, and showcasing practical operational applications for photonic processing.

Spiking neurons and neural networks constitute a fundamental building block for brain-inspired computing, which is poised to benefit significantly from photonic hardware implementations. In this work, we experimentally investigate an interconnected optical neuromorphic system based on an ultrafast spiking vertical cavity surface emitting laser (VCSEL) neuron and a silicon photonics (SiPh) integrated micro-ring resonator (MRR). We experimentally demonstrate two different functional arrangements of these devices: first, we show that MRR weight banks can be used in conjunction with the spiking VCSEL-neurons to perform amplitude weighting of sub-ns optical spiking signals. Second, we show that a continuously firing VCSEL-neuron can be directly modulated using a locking signal propagated through a single weighting MRR, and we utilise this functionality to perform optical spike firing rate-coding via thermal tuning of the MRR. Given the significant track record of both integrated weight banks and photonic VCSEL-neurons, we believe these results demonstrate the viability of combining these two classes of devices for use in functional neuromorphic photonic systems.

We report experimental and simulation results of a weighting scheme that encodes the weight imparted on an RF signal in its modulation frequency. This scheme significantly increases the multiply-accumulate compute density of microring-based Photonic Neural Networks and enables the implementation of large weight matrices.

In this talk, we report resonance tuning of a silicon nitride microring resonator structure using photochromic molecules. A slot waveguide structure and back-end compatible light molecule evaporation are used to enhance interaction of the molecules and optical mode. The device is interrogated in the optical C-band where the molecules exhibit low optical loss, but where a change in refractive index is present. Under UV illumination the resonance is observed to redshift, while under visible illumination the resonance blueshifts. Furthermore, the observed index shift is seen to be non-volatile. This constitutes a new way to optically reversibly trim and reconfigure high index contrast photonic integrated circuits for which a plethora of applications have been investigated recently.

Analog photonic information processing can be implemented with low chip area using wavelength-division multiplexed systems, which typically manipulate light using micro-ring resonators. Micro-rings are uniquely susceptible to thermal crosstalk, with negative system performance consequences if not addressed. Existing thermal sensitivity mitigation methods face drawbacks including high complexity, high latency, high digital and analog hardware requirements, and CMOS incompatibility. Here, we demonstrate a passive thermal desensitization mechanism for silicon micro-ring resonators exploiting self-heating resulting from optical absorption. We achieve a 49% reduction in thermal crosstalk sensitivity and 1 µs adaptation latency using a system with no specialized micro-ring engineering, no additional control hardware, and no additional calibration. Our theoretical model indicates the potential for significant further desensitization gains with optimized micro-ring designs. Self-heating desensitization can be combined with active thermal stabilization to achieve both responsiveness and accuracy or applied independently to thermally desensitize large photonic systems for signal processing or neural network inference.

Broadband analog signal processors utilizing silicon photonics have demonstrated a significant impact in numerous application spaces, offering unprecedented bandwidths, dynamic range, and tunability. In the past decade, microwave photonic techniques have been applied to neuromorphic processing, resulting in the development of novel photonic neural network architectures. Neuromorphic photonic systems can enable machine learning capabilities at extreme bandwidths and speeds. Herein, low‐quality factor microring resonators are implemented to demonstrate broadband optical weighting. In addition, silicon photonic neural network architectures are critically evaluated, simulated, and optimized from a radio‐frequency performance perspective. This analysis highlights the linear front‐end of the photonic neural network, the effects of linear and nonlinear loss within silicon waveguides, and the impact of electrical preamplification.

Controlling changes in the optical properties of photonic devices allows photonic integrated circuits (PICs) to perform useful functions, leading to a large breadth of applications in communications, computing, and sensing. Many mechanisms to change optical properties exist, but few allow doing so in a reversible, non-volatile manner. Without such mechanisms, power inefficiencies and use of external memory are inevitable. In this work, we propose and experimentally demonstrate reversible, non-volatile phase actuation of a silicon nitride PIC with thermally stable photochromic organic molecules vapor-deposited within a slot waveguide structure. The use of a high-core-index platform allows the photochemical phase actuation of a planar-resonator-based photonic memory unit, which enables positive and negative signal weighting and permits integrated spectroscopic analysis. We show properties of this all-optical memory for a silicon photonics platform, including low loss in the optical C-band, first-order photokinetics of the photoconversion, bidirectional scalable switching, and continuous tuning. Such features are critical for memories in analog applications such as quantum, microwave, and neuromorphic photonics, where bipolar weights, low loss, and precision are paramount. More generally, this work suggests that back-end-of-line-compatible vapor deposition of organic molecules into silicon photonic circuits is promising to introduce non-silicon-native functionality. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Developing compute platforms capable of performing computations at high speed is essential for data processing in the next generation of data centers and edge devices. A neuromorphic photonic accelerator on a silicon photonic platform is a promising solution. Compared to silicon photonic data communication transceiver modules, neuromorphic photonic accelerators constitute a large number of active and passive components and optoelectronic devices to handle the parallel processing. Thus, an increased number of optical and electrical interconnects are required, making the packaging of such processors challenging. Moreover, thermal and electrical crosstalk can dramatically degrade the performance of such processors. Thus, packaging a neuromorphic photonic accelerator for efficient processing and data movement requires careful considerations at the chip, module, and board levels. This work investigates the challenges and potential solutions for optical coupling, optical and electrical interconnections, processor-memory communication, and thermal and electrical cross-talk to develop neuromorphic photonic accelerators.

mmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous demonstrations of such systems have been limited by high latencies and the need for regular calibration. Here, we demonstrate real-time photonic blind interference cancellation using an FPGA-photonic system executing a zero-calibration control algorithm. Our system offers a greater than 200-fold reduction in latency compared to previous work, enabling sub-second cancellation weight identification. We further investigate key trade-offs between system latency, power consumption, and success rate, and we validate sub-Nyquist sampling for blind interference cancellation. We estimate that photonic interference cancellation can reduce the power required for digitization and signal recovery by greater than 74 times compared to the digital electronic alternative.

Broadcast-and-weight (BaW) photonic neural networks can process high-bandwidth signals with limited chip area, but they traditionally lack topological reconfigurability. We propose using a fully-connected recurrent BaW system as a topologically reconfigurable network and demonstrate a multi-layer feedforward network implemented on such a system.

Multiple-input multiple-output (MIMO) mmWave devices broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Conventional techniques for mitigating interference require additional space and power not generally available in handheld mobile devices. Here, we propose a photonic mmWave MIMO receiver architecture capable of interference cancellation with greatly reduced space and power needs. We demonstrate real-time photonic interference cancellation with an integrated FPGA-photonic system that executes a novel zero-calibration micro-ring resonator control algorithm. The system achieves sub-second cancellation weight determination latency with sub-Nyquist sampling. We evaluate the impact of canceller design parameters on performance, establishing that effective photonic cancellation is possible in handheld devices with less than 30 ms weight determination latency.

The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the carrier frequency, signal format, and channel conditions. However, previous electronic implementations did not fulfil this versatility due to the inherently narrow bandwidth of radio-frequency (RF) components, the high energy consumption of digital signal processors (DSP), and their shared weaknesses of low scalability. Here, we report a photonic BSS approach that inherits the advantages of optical devices and fully fulfils its “blindness” aspect. Using a microring weight bank integrated on a photonic chip, we demonstrate energy-efficient, wavelength-division multiplexing (WDM) scalable BSS across 19.2 GHz processing bandwidth. Our system also has a high (9-bit) resolution for signal demixing thanks to a recently developed dithering control method, resulting in higher signal-to-interference ratios (SIR) even for ill-conditioned mixtures.