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Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved. We have also proposed a method for automatic labelling of SMI fringes under variable optical feedback to facilitate the generation of a large training dataset. Specifically, we have trained two deep neural network models, namely Yolov5 and EfficientDet, and analysed the performance of these networks on various experimental SMI signals acquired by using different laser-diode-based sensors operating under different noise and speckle conditions. The performance has been quantified in terms of fringe detection accuracy, signal to noise ratio, depth of modulation, and execution time parameters. The impact of network architecture on real-time sensing is also discussed.

In this paper, a method based on the inherent event-based sampling capability of laser optical feedback interferometry (OFI) is proposed to assess the optical feedback factor C when the laser operates in the moderate and strong feedback regimes. Most of the phase unwrapping open-loop OFI algorithms rely on the estimation of C to retrieve the displacement with nanometric precision. Here, the proposed method operates in open-loop configuration and relies only on OFI’s fringe detection, thereby improving its robustness and ease of use. The proposed method is able to estimate C with a precision of <5%. The obtained performances are compared to three different approaches previously published and the impacts of phase noise and sampling frequency are reported. We also show that this method can assess C on the fly even when C is varying due to speckle. To the best of the authors’ knowledge, these are the first reported results of time-varying C estimation. In addition, through C estimation over time, it could pave the way not only to higher performance phase unwrapping algorithms but also to a better control of the optical feedback level via the use of an adaptive lens and thus to better displacement retrieval performances.

In this study, we show the capability of integrated Micro-Ring Resonators (MRRs) to perform frequency-to-amplitude (FM-to-AM) conversion of optical feedback interferometry (OFI) signals with improved signal-to-noise ratio compared to conventional AM OFI signals. Further, contrary to traditional OFI FM-to-AM conversion techniques using gas cell-based edge filters and free-space or fiber Mach-Zehnder Interferometers (MZI), integrated photonic processing offers greater compactness and perturbation resilience, enhancing noise performance through improved temperature control and immunity to parasitic mechanical vibrations. The OFI FM-to-AM conversion was performed with a fabricated silicon nitride MRR of radius 120 μm and a quality factor of 130,000. The FM-to-AM conversion factor achieved was 0.61 GHz −1 , with a noise equivalent displacement of only 4.9 nm for a 1 kHz bandwidth. This demonstration highlights the potential of integrated edge filters to replace traditional freespace and fiber architectures, more prone to environmental perturbations, in OFI signal processing for vibrometric applications.

We present a Self-Mixing (SM) interferometric laser displacement sensor that is capable of providing correct target measurements in real time, even when it is subject to extraneous parasitic movements. The sensor achieves such robustness by using an embedded MEMS Solid -State Accelerometer (SSA) that has been coupled with the laser sensor. The SSA thus measures the extraneous movement acting on the laser s ensor and this information is used to provide correct sensing. The proposed SSA-SM sensing system uses Consecutive-Samples based Unwrapping (CSU) to process the SM interferometric signal while a Digital Signal Processor (DSP) takes care of band-pass filtering, double integration as well as phase and gain corrections needed for the acceleration signal. Hence, a compact, real-time, precise and self-aligned SSA-SM sensor has been designed that has a displacement measurement precision of approximately 100 nm with a parasitic movement elimination of 31dB for a laser diode emitting at 785 nm.