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Digital-to-analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide-band telecommunication links for both civil and military applications. Since photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain-crossing such as digital-to-analog, and electronic-to-optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often require bulky discrete optical components and electric-optic transformation hence introducing inefficiencies. Here, we introduce a novel coherent parallel photonic DAC concept along with an experimental demonstration capable of performing this digital-to-analog conversion without optic-electric-optic domain crossing. This design hence guarantees a linear intensity weighting among bits operating at high sampling rates, yet at a reduced footprint and power consumption compared to other photonic alternatives. Importantly, this photonic DAC could create seamless interfaces of next-generation data processing hardware for data-centers, task-specific compute accelerators such as neuromorphic engines, and network edge processing applications.
With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that h
A high-rate yet low-cost air-to-ground (A2G) communication backbone is conceived for integrating the space and terrestrial network by harnessing the opportunistic assistance of the passenger planes or high altitude platforms (HAPs) as mobile base sta
Photonic analog to digital conversion offers promise to overcome the signal-to-noise ratio (SNR) and sample rate trade-off in conventional analog to digital converters (ADCs), critical for modern digital communications and signal analysis. We propose
In a growing number of applications, there is a need to digitize signals whose spectral characteristics are challenging for traditional Analog-to-Digital Converters (ADCs). Examples, among others, include systems where the ADC must acquire at once a
The influence on $gamma$-ray spectra of differential nonlinearities (DNL) in subranging, pipelined analog-to-digital converts (ADCs) used for digital $gamma$-ray spectroscopy was investigated. The influence of the DNL error on the $gamma$-ray spectra