140 research outputs found
Increasing Restorability for Local-to-Egress Restoration in GMPLS Controlled Networks with Limited Wavelength Conversion
A 40 Gb/s InP-monolithically integrated DPSK-demolulator enhanced by cross-gain-compensation in an SOA
We fabricated and experimentally tested a novel monolithically integrated Indium Phosphide optical circuit for differential phase-shift keying demodulation, which is robust to noise degradations of the received signal. The circuit consists of a one-bit-delay interferometer that demodulates the incoming signal and a semiconductor optical amplifier where the constructive and destructive demodulated outputs synchronously counter-propagate experiencing a reshaping effect. The novel optical circuit has been fabricated for 40 Gb/s signals, and the amplitude signal restoration is demonstrated by comparing the obtained output eye diagrams with those of a commercial fiber-based demodulator. We find a net improvement in the signal to noise ratio when the circuit is fed with a noisy input signal
The Orbital Angular Momentum of Light for Ultra-High Capacity Data Centers
The potential of orbital angular momentum (OAM) of light in data center scenarios is presented. OAMs can be exploited for short reach ultra-high bit rate fiber links and as additional multiplexing domain in transparent ultra-high capacity optical switches. Recent advances on OAM integrated photonic technology are also reported. Finally demonstration of OAM-based fiber links (aggregate throughput 17.9 Tb/s) and two layers OAM-WDM-based optical switches are presented exploiting OAM integrated components and demonstrating the achievable benefits in terms of size, weight and power consumption (SWaP) compared to different technologies
Signaling-based Joint Selection of Wavelengths and Regenerator Points in GMPLS-controlled WSONs
Photonic integrated reconfigurable linear processors as neural network accelerators
Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency
- …