Injection locking of optomechanical oscillators via acoustic waves

Injection locking is a powerful technique for synchronization of oscillator networks and controlling the phase and frequency of individual oscillators using similar or other types of oscillators. Here, we present the first demonstration of injection locking of a radiation-pressure driven optomechanical oscillator (OMO) via acoustic waves. As opposed to previously reported techniques (based on pump modulation or direct application of a modulated electrostatic force), injection locking of OMO via acoustic waves does not require optical power modulation or physical contact with the OMO and it can easily be implemented on various platforms. Using this approach we have locked the phase and frequency of two distinct modes of a microtoroidal silica OMO to a piezoelectric transducer (PZT). We have characterized the behavior of the injection locked OMO with three acoustic excitation configurations and showed that even without proper acoustic impedance matching the OMO can be locked to the PZT and tuned over 17 kHz with only -30 dBm of RF power fed to the PZT. The high efficiency, simplicity and scalability of the proposed approach paves the road toward a new class of photonic systems that rely on synchronization of several OMOs to a single or multiple RF oscillators with applications in optical communication, metrology and sensing. Beyond its practical applications, injection locking via acoustic waves can be used in fundamental studies in quantum optomechanics where thermal and optical isolation of the OMO are critical

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe