Partial coherence enhances parallelized photonic computing.

Advancements in optical coherence control1-5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6-8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9-11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Mode Conversion Trimming in Asymmetric Directional Couplers Enabled by Silicon Ion Implantation.

An on-chip asymmetric directional coupler (DC) can convert fundamental modes to higher-order modes and is one of the core components of mode-division multiplexing (MDM) technology. In this study, we propose that waveguides of the asymmetric DC can be trimmed by silicon ion implantation to tune the effective refractive index and facilitate mode conversion into higher-order modes. Through this method of tuning, transmission changes of up to 18 dB have been realized with one ion implantation step. In addition, adjusting the position of the ion implantation on the waveguide can provide a further degree of control over the transmission into the resulting mode. The results of this work present a promising new route for the development of high-efficiency, low-loss mode converters for integrated photonic platforms, and aim to facilitate the application of MDM technology in emerging photonic neuromorphic computing.

Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation.

Photonic integrated circuits (PICs) are revolutionizing the realm of information technology, promising unprecedented speeds and efficiency in data processing and optical communication. However, the nanoscale precision required to fabricate these circuits at scale presents significant challenges, due to the need to maintain consistency across wavelength-selective components, which necessitates individualized adjustments after fabrication. Harnessing spectral alignment by automated silicon ion implantation, in this work scalable and non-volatile photonic computational memories are demonstrated in high-quality resonant devices. Precise spectral trimming of large-scale photonic ensembles from a few picometers to several nanometres is achieved with long-term stability and marginal loss penalty. Based on this approach, spectrally aligned photonic memory and computing systems for general matrix multiplication are demonstrated, enabling wavelength multiplexed integrated architectures at large scales.