The growth of 3D imaging across a range of sectors has driven a demand for beam steering technology. Fields as diverse as autonomous vehicles and medical imaging can benefit from a high speed, adaptable method of beam steering. We present a monolithic, submicrosecond electro-optic switch as a solution toward the need for reliability, speed, dynamic addressability, and compactness. Here, we demonstrate a laboratory-scale, solid-state light detection and ranging system, using the electro-optic switch to launch modulated coherent light into free space and then to collect the reflected signal. We use coherent detection of the reflected light to simultaneously extract the range and axial velocity of targets at each of the several electronically addressable output ports.

Optical scanners, capable of high-speed optical beam pointing, are essential for many imaging techniques, including light detection and ranging (LIDAR) and medical imaging applications. The first commercial demonstrations of multipixel light detection and ranging (LIDAR) sensors relied on mechanical spinning mirrors, which are cumbersome and lack dynamic addressability. Similarly, full-field optical coherence tomography relied on sample stage movement or mechanical mirror steering to scan a sample. Recent advances have moved to simple integrated beam scanning techniques, including micro-electro-mechanical mirrors,1,2 optical phase arrays,3–5 and vertical-cavity surface-emitting lasers.6,7 Other major approaches include liquid crystal electro-optic scanners,8–10 electro-optic beam deflectors,11–13 and spectral scanning.14–16 These beam scanning techniques have allowed improved sensing performance by increasing the size and refresh rate of the generated point cloud. Most of these beam scanning technologies still limit the point cloud size and refresh rate due to speed limitations, with the notable exception of indium phosphide optical phase arrays, which have angle sweep rates of >10°/μs.17 

An alternative approach to spatial beam manipulation is to use a device with distinct separate spatial output modes to perform a discrete “point-by-point” scan rather than a continuous sweep. A reconfigurable waveguide network can perform such a discrete scan. This approach ensures high speed, side-lobe-free, single mode, and single wavelength beam steering with the field of view and resolution set instead by the output optics. Such discrete scanning has previously been demonstrated with a silicon photonic integrated circuit, where the output channel is controlled thermally.18 Here, we demonstrate a fiber-to-free-space switch network based on an integrated electro-optic device, which enables high speed, solid-state, single-mode output optical beam steering and light collection. We use this capability to demonstrate integrated laser ranging and velocimetry using coherent, modulated light and detection.

In this paper, we describe the design, fabrication, and operation of a reconfigurable waveguide network for beam steering before moving onto its deployment in a laboratory scale demonstration of coherent laser ranging and velocimetry. We then discuss our test target, and the measurement protocol employed before looking at the performance and limitations of such a system, and conclude with a brief outlook for discrete beam scanning for LIDAR.

The switch network is constructed from a series of directional couplers and is fabricated by the annealed proton exchange technique in congruent lithium niobate.19,20 The splitting ratio of each directional coupler is tunable between 0% and 100% by applying a voltage to electrodes patterned around two evanescently coupled waveguides.21 Full fabrication details are included in the supplementary material. For this demonstration, we create a switch with three output channels and a total loss of ∼4 dB. We characterize the frequency response of a representative electro-optic directional coupler by using it to amplitude modulate a laser beam. The modulated light is detected on a fast photodiode, and the frequency response of the photodiode signal is shown in Fig. 1(a), demonstrating switching rates up to 300 MHz, limited by electrode design. With improved designs, the response of such a system should reach the gigahertz range.

FIG. 1.

(a) Representative optical response of one directional coupler switch with an applied electronic signal measured using a photodiode (DET08CFC, Thorlabs, 5 GHz), with a sine wave injected from a waveform generator(E4432-B, Agilent, 250 kHz–3 GHz). The frequency response of the photodiode is not removed from the displayed signal. Directional coupler switch refers to a single point in the waveguide network where light may be directed between two different outputs. (b) AMCW LIDAR setup, full details in the text, diagram not to scale. Here, the first (signal) channel is shown at the top and the second (local oscillator) channel shown at the bottom. 3 dB DC—3 dB directional coupler, EOM—electro-optic modulator, HD—homodyne detector, and VT—tangential velocity of target. Light from the switching network is directed by a lens that maps the spatial location of the switch to a direction in free space. Scale bars on the monolithic optical switching network diagram represent the whole length and width of the chip. (c) Timing diagram for signal modulation using EOMs, where the high level represents when the EOM transmits light.

FIG. 1.

(a) Representative optical response of one directional coupler switch with an applied electronic signal measured using a photodiode (DET08CFC, Thorlabs, 5 GHz), with a sine wave injected from a waveform generator(E4432-B, Agilent, 250 kHz–3 GHz). The frequency response of the photodiode is not removed from the displayed signal. Directional coupler switch refers to a single point in the waveguide network where light may be directed between two different outputs. (b) AMCW LIDAR setup, full details in the text, diagram not to scale. Here, the first (signal) channel is shown at the top and the second (local oscillator) channel shown at the bottom. 3 dB DC—3 dB directional coupler, EOM—electro-optic modulator, HD—homodyne detector, and VT—tangential velocity of target. Light from the switching network is directed by a lens that maps the spatial location of the switch to a direction in free space. Scale bars on the monolithic optical switching network diagram represent the whole length and width of the chip. (c) Timing diagram for signal modulation using EOMs, where the high level represents when the EOM transmits light.

Close modal

We deploy this switch network for beam steering in an amplitude modulated continuous wave (AMCW) LIDAR system. LIDAR systems are of great interest for automotive hazard detection and navigation, and a number of techniques have been implemented in practice. These include pulsed time of flight, frequency modulated continuous wave (FMCW), flash, and coherent flash protocols. Any system used for automotive purposes must support an encoding that can ignore interfering signals from other road users, such as the output of a chaotic laser for a 3D LIDAR system.24 Pulsed systems can achieve this easily using random amplitude pulse encoding; however, flash and FMCW systems are reliant on spectral separation, a scheme not suitable to produce millions of unique units. AMCW facilitates coherent time-of-flight plus Doppler analysis of the return signal, using eye-safe average and peak output powers. Our AMCW implementation allows for the simultaneous measurement of range and velocity across discrete pixels in one dimension using only a single coherent detector. Additionally, the switch architecture allows for temporal multiplexing of the detection electronics since all pixels are measured by a single detector. The full scheme is shown in Fig. 1(b).

A low noise continuous wave laser at 1550 nm (Koheras Boostik, NKT Photonics) is split by a 3 dB coupler between two commercially available electro-optic modulators (EOMs). One EOM (EOM1, Thorlabs, LN81S/FC), which we will refer to as the signal EOM, is modulated to produce a square-wave, coherent pulse train on the output light, with a repetition rate of fr=10 MHz and a pulse width of 10 ns. This pulse train is the amplitude modulation, which provides time-of-flight sensitivity. Including intrinsic losses, the EOM transmission is −18.8 dB, which we note is unusually low. The second channel's EOM (EOM2, Lucent 2623NA) is modulated to produce the inverse pulse sequence of the first, as demonstrated in Fig. 1(c). The total transmission of the second EOM is −6.1 dB. This second channel is used to create the local oscillator arm for homodyne detection. This modulation of the local oscillator signal reduces the detection of the back reflection from the electro-optic switch and allows an increase in the dynamic range of the LIDAR measurement.

The output from the signal EOM is passed through an optical circulator and into the custom-made 50 mm × 8 mm × 1 mm monolithic 1-to-Nout LiNbO3 switch network. The switch network we fabricated has 10 physical output channels available. For demonstration purposes, we connected three Rigol DG1022z arbitrary waveform generator outputs so that we could address 3 of the output channels. That is, for demonstration purposes, we address Nout = 3 output channels. The output channels of the switch network are collimated onto a remote target by a 25 mm diameter, 25 mm focal length lens placed 25 mm from the end of the switch network. This lens sets the angle at which each switch output is directed [see Fig. 1(b)]. After reflection from the target, the returned light is collected at the same switch port from which it was launched. The collected light is recombined with the other arm of the 3 dB splitter, which acts as a local oscillator, and is detected using a homodyne detector with a bandwidth of 100 MHz.21 Distance and velocity can be retrieved from the Fourier spectrum of the homodyne detector signal, which is captured at a sampling rate of 200 megasamples per second (MSPS) using an oscilloscope.

In this demonstration, the test target was a spinning drum of radius 64 ± 1 mm with a diffuse surface placed approximately 4.7 m away from the lens, the length of the laboratory. A rotating target provides a simple method to demonstrate simultaneous ranging and velocimetry.22,23 In the current setup, the transverse separation between light from channels 1 and 2 is 9.4 mm and between channels 2 and 3 is 4.7 mm at the target. These parameters can be varied by using a different lens.

The switch network is programed to launch and collect light from each output channel in cyclic order, i.e., a measurement is taken from channel 1, then channel 2, and so on. The electronic signals applied to the electrodes around each directional coupler are ±10 Vpp square waves, and for this demonstration, they are sourced from two synchronized Rigol DG1022z arbitrary waveform generators. “For these measurements, the switching rate between different outputs is set to 10 kHz, i.e., sweep rate is 10 kHz, with a duty cycle of 10% per channel. Thus, our demonstration acquires 3×104 points per second.” Our acquisition time per point (dwell time per pixel) is 10μs. The switching time from one pixel to another was ∼100 ns, limited by the bandwidth of the arbitrary waveform generator. The extinction ratio of each switch is approximately 10 dB at 10 kHz, improvable using a more complex electrode design.24 The switches do not respond in a stable manner below 500 Hz and therefore a DC extinction ratio cannot be measured. A timing signal is sent from the switch control electronics to the oscilloscope to indicate which channel is active.

Both range and velocity information can be extracted from the homodyne measurement of the collected light: time-of-flight ranging information is encoded in the time-delay of the pulse train relative to the launch time. From the relative phase of the signal reflected off the input of the switch network and the return signal, the time delay and therefore the distance to target can be calculated. Axial velocity of the target is encoded in the Doppler frequency shift from the relative motion of the object; given a Doppler frequency shift Δfd, the velocity of the target is given by v=λΔfd=1.55×106Δfdms1. These quantities are extracted from the Fourier transform of the homodyne signal, examples of which are shown in Fig. 2.

FIG. 2.

Sample return waveform from the AMCW LIDAR system, showing the Doppler frequency components on a linear scale. Each color represents a different channel with the data taken sequentially. Doppler frequency shift (Δfd) peaks indicated. The full width at half maximum of peaks is 66 kHz. The inset shows large peaks at multiples of 10 MHz caused by the reflected signal from the input of the optical switch. These are used as a timing reference. Other smaller peaks are the signal returned from the target that has been Doppler shifted. The 5 MHz range was chosen to show Doppler peaks from carrier frequency only.

FIG. 2.

Sample return waveform from the AMCW LIDAR system, showing the Doppler frequency components on a linear scale. Each color represents a different channel with the data taken sequentially. Doppler frequency shift (Δfd) peaks indicated. The full width at half maximum of peaks is 66 kHz. The inset shows large peaks at multiples of 10 MHz caused by the reflected signal from the input of the optical switch. These are used as a timing reference. Other smaller peaks are the signal returned from the target that has been Doppler shifted. The 5 MHz range was chosen to show Doppler peaks from carrier frequency only.

Close modal

With the aforementioned sweep rate and duty cycle, 10 000 samples are taken per channel per measurement. The rate at which the system can complete a full measurement set is the sweep rate (10 kHz), limited by the required frequency resolution. The frequency (velocity) resolution is calculated from the full width at half maximum of the lowest frequency Doppler peak, which was ±33kHz(±25mm/s). The position resolution is set by the sampling rate [200 mega-samples per second (MSPS), which implies a 1.5 m round-trip distance uncertainty, which corresponds to a ranging uncertainty of ±0.38 m]; however, the random phase of the noise in the signal increases this uncertainty proportional to the signal to noise ratio (SNR).

From the Doppler LIDAR measurements, using a known radius, the tangential velocity of the spinning target can be calculated as well as the angle-of-incidence for the light. The surface velocity of the target was set to vs=15.3±0.1m/s, and based on the angle of incidence, we calculate the axial target velocity vt=vssin(θinc) to compare with the Doppler inferred velocity. These measured and computed quantities are summarized in Table I, showing very good agreement between the measured and inferred velocities.

TABLE I.

Summary of measurement results for three channel AMCW LIDAR. Range uncertainty limited by both the sampling rate (200 MSPS) and SNR. The sampling rate implies ±0.38 m uncertainty in ranging, as described in the main text. The target was located a distance D =4.7 m from the output.

ChannelIncidence angle, θincSNR (linear/dB)Velocity ms Doppler-measured, [calculated, vt=vssin(θinc)]Range (m)
23° 5.1/7.1 5.97 ±0.03, (5.98) 4.5 ± 0.6 m 
19° 2.7/4.3 4.91 ±0.03, (4.98) 5.8 ± 1.3 m 
10° 3.1/4.9 2.64 ±0.03, (2.66) 4.5 ± 1.1 m 
ChannelIncidence angle, θincSNR (linear/dB)Velocity ms Doppler-measured, [calculated, vt=vssin(θinc)]Range (m)
23° 5.1/7.1 5.97 ±0.03, (5.98) 4.5 ± 0.6 m 
19° 2.7/4.3 4.91 ±0.03, (4.98) 5.8 ± 1.3 m 
10° 3.1/4.9 2.64 ±0.03, (2.66) 4.5 ± 1.1 m 

For ranging, in our demonstration, the return signal was sufficiently weak that ranging data based on the phase shift were subject to large uncertainty due to the low SNR. However, time-domain analysis of the return signal is capable of giving improved ranging accuracy. The accuracy can be further improved by using a higher sampling rate to improve the timing resolution or increasing the acquisition time or output power to improve the SNR. It would be feasible with fast electronics to increase the sampling to 5 gigasamples per second (GSPS), which would improve the position to ±3 cm, provided the SNR is sufficiently high.

The SNR in our experiment is limited by the low output power. The average power of each switch network output is approximately 200 μW, limited by the maximum input power of the EOMs (100 mW) and their transmission. The total transmission of components before the switch network is around −23 dB so that input power to the electro-optic switch is ∼500 uW. To obtain higher output powers and therefore better signal to noise ratios, a different EOM with lower loss and higher power handling capabilities should be used to deliver the maximum input power to the switch (∼300 mW). A low noise erbium doped fiber amplifier may also be used after the EOM in the signal arm to achieve a similar result. This was attempted; however, the available amplifier was too noisy and so we report only unamplified data.

Because of the low pulse repetition rate, the unambiguous velocity measurement range is only 7.75ms1, and the unambiguous range measurement interval is only 15 m. The ranging accuracy and interval are small as this is designed as a simple laboratory demonstrator of the beam steering capabilities. This beam steering method is agnostic to the ranging and velocimetry method. The angular resolution and steering angle range of this system is determined by the output lens system that is used. In this setup, the angular separation between channels 1 and 2 is tan1spotseparationattargetdistancetotarget1×103 radians and between 2 and 3 is 2×103 radians. The angular resolution is limited to the steering angle range divided by the number of output channels.

An advantage of a discrete beam steering scheme such as the one presented here is the possibility for distributed sensing heads for LIDAR. As the outputs of the electro-optic switch may be recollected into fiber, the outputs can be routed to different places around a central control unit. This means the “sensor head” can be extremely small, unobtrusive, and easily replaced. The transmit/receive module and the switch network may be integrated onto a daughter board connected to the central unit. This is ideally suited to harsh environment sensing, where any external sensor head could be damaged, and must be cheaply and easily replaceable. Furthermore, the requirement of only one set of detection electronics for multiple sensors can reduce the total sensor cost for automotive LIDAR.

We have demonstrated the utility of high speed switch networks for free-space beam scanning applications by creating a multipixel LIDAR system with a three channel switch where the point cloud size is not limited by the beam-steering rate. This scheme has several advantages including its speed, modularity, dynamic addressability, single-mode output, and compatibility with various coherent light modulation techniques. The approach we describe is potentially scalable up to ∼1000 output channels per chip using smaller footprint thin-film ridge waveguides in lithium niobate.25 Our optical design natively addresses a single spatial dimension, although methods exist for mapping from 1D to 2D, such as26 to create a chip with 2D scanning, one direction discrete, and the other continuous. With standalone lasers, individual fiber components, and control electronics supplied by commercial signal generators, this demonstrator is currently “trolley-sized.” With suitable integration of laser and fiber components into a photonic integrated circuit and electronics onto a single circuit board, the total system has the potential to be smaller than a piece of A5 paper.

See the supplementary material for fabrication details of the reconfigurable waveguide network.

The authors thank Stefan Morley for electronics support. B.H. was supported by the Australian Government Research Training Program Scholarship. This research was financially supported by the Griffith University Research Infrastructure Programme, the Australian Research Council Centre of Excellence for Quantum Computation and Communication Technology (CQC2T, No. CE170100012), T.M.S. and M.A.B. were supported by the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, No. CE170100009), T.M.S. was supported by the Australian Research Council Future Fellowship (No. FT140100952), and M.L. was supported by the Australian Research Council Future Fellowship (No. FT180100055). This work was performed in part at the Queensland node of the Australian National Fabrication Facility, a company established under the National Collaborative Research Infrastructure Strategy to provide nano- and microfabrication facilities for Australia's researchers.

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Supplementary Material