Automatic Detection of Multi-Modality in Self-Mixing Interferometer

Abstract

Laser feedback based self-mixing interferometry (SMI) has been demonstrated for diverse metric sensing applications. Typically, SMI sensors are based on such laser diodes (LDs) which provide mono-modal emission resulting in SMI signals in which each interferometric fringe occurs due to change in optical path length of λ/2, where λ is emission wavelength. However, in case multiple laser modes undergo SMI, then each mode contributes its own set of fringes. As LDs can emit multiple modes under variable operating conditions, so, non-detection of multiple SMI modes can cause drastic increase in measurement error due to wrong interpretation of fringes. Previously, detection of multiple laser modes undergoing SMI was achieved by adding spectroscopic instruments to the SMI setup. This, however, compromises the inherent simplicity of SMI sensing. In this work, an automatic SMI based multi-modality detection method is proposed which is able to detect if multiple modes of deployed LD are undergoing SMI and are contributing additional fringes within the SMI signal under variable sensing conditions. Such detection enables correct interpretation of SMI fringe count and can be used to signal occurrence of modehopping or secondary mode excitation. The method uses an artificial neural network, able to automatically identify uni-, bi-, or tri-modal SMI signals. Two different LDs (emitting at 637 nm and 650 nm) were used to acquire 131 experimental uni-, bi-, and tri-modal SMI signals for variable operating conditions and target vibration amplitude. The proposed system has achieved modality detection accuracy of 98.57% on 70 unseen experimental SMI signals.

Detection of Multimodal Fringes for Self-Mixing-Based Vibration Measurement

“…Such multi-modality has been reported for SM sensors based on in-plane semiconductor Fabry-Perot lasers [11,12], vertical-cavity surface emitting lasers [13] and quantum cascade laser [14]. The corresponding fringe multiplicity of such multi-modal SM signals needs to be appropriately detected [16], interpreted (as each fringe now corresponds to a remote target motion of λ/2m, where m is the number of modes undergoing SM [12]), and processed to correctly retrieve the target motion. This laser modality is a function of LD's operating current [14,15], and temperature [12] while length of external cavity can also affect the multi-modality of the SM laser sensor [11,13].…”

“…1), a feature which may be used to improve the SM sensor resolution from λ/2 (mono-modal) to λ/4 (bi-modal) or λ/6 (tri-modal) [12]. However, the said higher measurement resolution associated with multi-modal SM signals cannot be achieved without correctly identifying the laser modality [16] and detecting these multi-modal SM fringes.…”

Versatile Multimodality Imaging System Based on Detectorless and Scanless Optical Feedback Interferometry—A Retrospective Overview for A Prospective Vision

“…The Rubik-like cube illustrated in Figure 1 symbolizes our idea of the OF expansion capability. We represented the versatility of OF systems on the third axis: from the simplest single-channel to multiple parallel channels, OF has matured the chance of going multi-modal [ 19 , 20 , 21 ]. Axes 1 and 2 measure the information increase due to improving time and space resolution.…”

Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor

“…In order to design low-cost SM sensors, usually commercial off the shelf (COTS) laser diodes (LD) are preferred. However, due to OF inside the active laser cavity, such low-cost monomodal LDs are prone to mode switching (as a function of operating conditions [9][10][11] ) resulting in multi-modal SM signals in which more than one laser mode undergoes SM. As a consequence, each interferometric fringe can no more be assumed to correspond to a remote displacement of λ/2 (where λ is the wavelength of LD), because in case of bimodal or tri-modal SM, an individual SM fringe does not correspond to a displacement of λ/2 anymore (see Fig.…”

“…Recently, a method based on an artificial neural network was proposed to classify mono-, and multi-modal SM signals with success rate of 98.75% [24] . However, this neural network based approach requires hand-crafted feature engineering.…”