The ever-increasing demand for integrated, low power interconnect systems is pushing the bandwidth density of CMOS photonic devices. Taking advantage of the strong Franz-Keldysh effect in the C and L communication bands, electro-absorption modulators in Ge and GeSi are setting a new standard in terms of device footprint and power consumption for next generation photonics interconnect arrays. In this paper, we present a compact, low power electro-absorption modulator (EAM) Si/GeSi hetero-structure based on an 800 nm SOI overlayer with a modulation bandwidth of 56 GHz. The device design and fabrication tolerant process are presented, followed by the measurement analysis. Eye diagram measurements show a dynamic ER of 5.2 dB at a data rate of 56 Gb/s at 1566 nm, and calculated modulator power is 44 fJ/bit.
In this review we present some of the recent advances in the field of silicon nitride photonic integrated circuits. The review focuses on the material deposition techniques currently available, illustrating the capabilities of each technique. The review then expands on the functionalisation of the platform to achieve nonlinear processing, optical modulation, nonvolatile optical memories and integration with III-V materials to obtain lasing or gain capabilities.
The explosive growth of deep learning applications has triggered a new era in computing hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this realm, integrated photonics have come to the foreground as a promising energy efficient deep learning technology platform for enabling ultra-high compute rates. However, despite integrated photonic neural network layouts have already penetrated successfully the deep learning era, their compute rate and noise-related characteristics are still far beyond their promise for high-speed photonic engines. Herein, we demonstrate experimentally a noise-resilient deep learning coherent photonic neural network layout that operates at 10GMAC/sec/axon compute rates and follows a noise-resilient training model. The coherent photonic neural network has been fabricated as a silicon photonic chip and its MNIST classification performance was experimentally evaluated to support accuracy values of >99% and >98% at 5 and 10GMAC/sec/axon, respectively, offering 6× higher on-chip compute rates and >7% accuracy improvement over state-of-the-art coherent implementations.
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