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Abstract
A midinfrared single-photon-counting lidar at 3 µm is presented. The 3 µm photons were upconverted to 790 nm in a periodically poled rubidium-doped ${\rm{KTiOP}}{{\rm{O}}_4}$ crystal through intracavity mixing inside a 1064 nm ${\rm{Nd}}{:}{{\rm{YVO}}_4}$ laser and detected using a conventional silicon single-photon avalanche detector (SPAD). The lidar system could distinguish 1 mm deep features on a diffusely reflecting target, limited by the SPAD and time-tagging electronics. This technique could easily be extended to longer wavelengths within the transparency of the nonlinear crystal.
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1. INTRODUCTION
There are multiple benefits of operating a lidar in the mid-infrared (MIR) spectral region, such as higher transmission through scattering media, like smoke or fog [1,2] and access to the molecular absorption lines in the fingerprint region [3], which allows for range-resolved gas measurements [4]. An efficient technique to achieve high sensitivity and range resolution in lidar measurements is time-correlated single-photon counting (TCSPC) [5,6]. This technique requires detectors sensitive enough to be triggered by single photons and yields a response with a high temporal resolution, which through time of flight gives the high range resolution. One such detector is the single-photon avalanche diode (SPAD) [7], and those based on silicon have low jitter, short dead-time, and operate at room temperature [8]. However, they only work in the visible and near-infrared (NIR), from roughly 400 to 1100 nm. There are SPADs based on other materials, such as Ge and InGaAs, which operate at longer wavelengths [9,10]. These are mostly limited to below 2 µm and suffer from longer dead-time due to after-pulsing and require cooling to reduce dark count rates. This further increases the after-pulsing problem and dead-time as a result [10]. Another type of detector for single-photon detection is the superconducting nanowire single-photon detector (SNSPD) [11,12]. SNSPDs mostly operate below 2.5 µm and require cryogenic cooling. Recent research, however, has shown single-photon detection at 10 µm with SNSPD but operating at temperatures of 0.85 K [13]. Comparison of figures of merit (defined as a ratio of detection efficiency to the product of dark count rate and jitter) for single-photon detectors reveals that Si-SPADs in their optimum sensitivity range remain the best choice for TCSPC applications [10].
To circumvent the need for detectors operating directly in the MIR region, while still performing the lidar measurements in that region, it is possible to employ upconversion detection [14,15]. The MIR radiation is then upconverted to the visible or NIR by sum-frequency generation (SFG), and conventional Si-SPADs can be used for detection. This approach combines high resolution in the measurement with operation at room temperature, while maintaining the MIR lidar advantages. Since the MIR radiation likely will have low intensity, the SFG process needs to be of high efficiency. However, it all depends on laser power, effective nonlinearity, and length of the nonlinear crystal as well as the focusing condition. A colinear interaction is possible by utilising quasi-phase matching (QPM), which also allows any desired MIR wavelength to be addressed within the transparency range of the nonlinear crystal [16]. As the conversion efficiency scales linearly with the power of the mixing laser, a high power is desirable. This can be achieved using a pulsed laser [17]; however, in a lidar setup, where the arrival time of photons is unknown beforehand, it is highly unsuitable. A better solution is to put the nonlinear crystal inside the cavity for the mixing laser and thereby exploiting the high intracavity power [18]. Previously, upconversion lidar has been accomplished close to 1.55 µm [19,20]. Photon-counting lidars with SNSPDs were demonstrated at 2.3 µm [21] and shorter wavelengths [22] but required liquid helium cooling of the detector. In previous work, we demonstrated an upconversion lidar at 2.4 µm with 42 ps temporal resolution using microscope slides as specular targets [23].
In this work, we demonstrate 3D lidar imaging above 3 µm wavelength of a diffusely reflecting target through photon counting at room temperature. To the best of our knowledge, this is the first time that a photon-counting lidar above 3 µm has been demonstrated.
2. EXPERIMENTAL SETUP
The experimental setup consists of three main parts, namely, the MIR source, upconversion stage, and Si-SPAD with accompanying electronics.
The MIR pulses were generated using an optical parametric amplifier (OPA). The OPA was pumped by 200 fs long pulses at 1033 nm (11 nm FWHM) with 1.5 µJ pulse energy and 1 MHz repetition rate. The seed used for the OPA was an in-house built laser, which was tuneable around 1560 nm with below 0.07 nm linewidth (limited by the resolution of the spectrometer) [24]. The seed was set to 1569 nm, and it had a power of 30 mW. The OPA stage utilized a periodically poled ${\rm{KTiOAs}}{{\rm{O}}_4}$ (PPKTA) crystal with an 8 mm long grating with 39.5 µm period, poled in-house [25]. The generated signal and idler had wavelengths of 1555 nm and 3.07 µm, respectively. The idler wavelength could not be measured directly with the spectrometer and was derived from the measured signal and pump wavelengths. The unseeded parametric gain in the OPA crystal was centered at 1540 nm. This is what caused the OPA signal to be blueshifted compared with the seed wavelength. To remove the remaining pump, two 5 mm thick silicon windows were inserted in the beam path at Brewster angle. This was done to avoid having the pump interfering with the experiment. A pinhole was used to reduce the size of the generated idler and signal beams and removed any off-axis radiation. After filtering, the idler had a pulse energy of about 3 nJ. Simulations with the software SNLO (AS-Photonics) estimate the idler pulse duration to 300 fs with a bandwidth of 90 nm.
In the upconversion stage, the MIR photons were upconverted to 790 nm in a periodically poled RKTP (PPRKTP) crystal by intracavity SFG in a ${\rm{Nd}}{:}{{\rm{YVO}}_4}$ laser operating at 1064 nm. The PPRKTP sample was fabricated in-house and had an 8 mm long grating with a period of 26.7 µm, yielding an acceptance bandwidth of roughly 25 nm for pulses with a duration of more than a few ps and 80 nm for 300 fs pulses due to temporal walk-off between the generated and idler beams. The crystal had antireflective (AR) coatings for 3 µm, 790 nm, and 1064 nm. The ${\rm{Nd}}{:}{{\rm{YVO}}_4}$ crystal was pumped at 808 nm and had AR coatings for both 808 and 1064 nm. The U-shaped laser cavity, shown in Fig. 1, was formed by a mirror (M1) with 75 mm radius of curvature (ROC), which was AR-coated for 808 and highly reflective (HR) for 1064 nm, and an output coupler (M4) with 99% reflectivity for 1064 nm with a ROC of 200 mm. Additionally, the cavity had two flat folding mirrors (M2 and M3), which were HR-coated for 1064 nm and AR-coated for 3 µm and 790 nm. The 1064 nm beam had a radius of 100 µm inside the PPRKTP crystal. During the experiments, the upconversion cavity was pumped by 3.4 W of 808 nm radiation, resulting in 38 W of intracavity power, measured from the 1% output coupling. With the used nonlinear crystal, intracavity power, and beam waist, the theoretical photon upconversion efficiency of the SFG process would be about 2%. It can be further increased by increasing the pump intensity or the interaction length in the nonlinear crystal.
Fig. 1. Sketch of the upconversion cavity where the MIR light (from left) is upconverted to 790 nm. The MIR, the 1064 and 808 nm pump, and the upconverted light are illustrated as bright red, yellow, dark red, and green beams, respectively.
The upconverted photons were detected with a Si-SPAD (Micro Photon Devices, PDM-series) together with a time-tagging unit (PicoHarp 300, PicoQuant). The arrival times were registered with 4 ps bin size for histogramming. The photon-detection efficiency of the detector at 790 nm was about 15% with a dead-time of 88 ns.
An illustrative sketch of the whole experimental setup can be seen in Fig. 2. The OPA pump was combined with the seed and focused into the PPKTA crystal with a 500 mm lens. The generated idler was collimated with a 200 mm lens and guided to the target using metallic mirrors. The idler was focused onto the target using a second 200 mm lens. The scattered light from the target was collected using a 1 in. lens with a focal length of 100 mm, positioned about 1 m from the target, and it imaged the scattering point on the target into the upconversion crystal. To perform a 3D scan of an object, a two-axis translation stage was employed, moving the target while keeping the laser stationary. All lenses used for the idler were ${\rm{Ca}}{{\rm{F}}_2}$ lenses. The generated upconverted light had to be filtered to remove small amounts of 1064, 808, and 532 nm (parasitic second-harmonic generation) leaking through the cavity mirrors, since the Si-SPAD is sensitive for these wavelengths as well. The filtering was performed by dispersive elements to spatially separate the wavelengths and with an optical fiber used for collection of the upconverted photons working as a pinhole. A half-wave plate was inserted after the upconversion stage to reduce the reflection loss when being filtered by a SF11 prism. To further separate the wavelengths, a diffracting grating (1200 lines/mm) was used. The upconverted light was coupled into a 62.5 µm core-diameter graded-index fiber, which was connected to the detector that also housed a 10 nm broad bandpass filter to remove ambient background light. The bandpass filter had a transmission of 50% at 790 nm. To obtain the time information, the TCSPC start signal was taken from a photodiode triggered by scattered light from the OPA pump; the stop signal was the triggered Si-SPAD pulse.
When a lidar trace had been collected, a function was fitted to the data, with the background subtracted, in order to estimate the range. Two different simple fits were tested. To evaluate the system, 3D mapping of different objects was performed. These were 3D-printed cubes with square indentations with various depths. The surface of each cube was covered with normal printer paper to obtain a more uniform surface. The paper had a matte finish, an opacity of 93%, and a whiteness of 161 CIE. By varying the 808 nm pump power while hitting a single spot of one of the objects, it was possible to study how the system performed at different intracavity 1064 nm powers. A lidar measurement with an integration time of 100 s was used to study how the standard deviation of the estimated position varied with integration time. For the 3D scans, an integration time of 1 s per pixel was used.
The detection of the whole system can be expressed as
where ${\eta _{\rm{SPAD}}}$ is the efficiency of the SPAD (15%), ${\eta _{\rm{conv}}}$ is the efficiency of the conversion stage (2%), and $\delta$ is the optical loss through the system. The main contribution to the loss is the bandpass filter (50% loss). This gives a theoretical conversion efficiency of the system of 0.15%.3. RESULTS AND DISCUSSION
The number of pulses detected shows linear dependence on intracavity power. Typically, 2% of the pulses from the target were detected at full intracavity power, 38 W, while no pulses could be detected when the cavity was unpumped. Nonetheless, the linear dependence shows that the lidar measurement is performed at 3 µm and that the detector was not triggered by a weak remaining pump from the OPA or any stray light.
The measurements suffered from background noise that did not depend on the pump power nor on the 1064 nm laser radiation. Hence, it was not caused by insufficient filtering of the pump or the 1064 nm radiation nor by thermal radiation in the room. Instead, we found that it appeared as soon as the voltage source for the 808 nm pump diode was switched on, even when the current was significantly below threshold. This suggests that it was below threshold spontaneous emission from the diode at a wavelength close to the upconverted wavelength, as it could not be filtered out by the prism and grating. The use of two dispersive elements did not improve the situation compared with a single dispersive element. The grating was used to ascertain no leakage of photons at other wavelengths into the fiber.
The noise gave a background of $8.5 \cdot {10^6} \;{\rm counts/s}$. To ensure the detector was not saturated by the background due to the 88 ns dead-time, the detector was time-gated to start measuring roughly 1 ns before the arrival of the signal. The signal was initially found without the time-gate using neutral density optical filters. The background noise could easily be avoided in future experiments by pumping the ${\rm{Nd}}{:}{{\rm{YVO}}_4}$ laser crystal with a 880 nm diode laser instead or by having the pump counterpropagating with the upconverted light in the cavity.
Example of four lidar traces from the diffuse target with subtracted backgrounds along with their fits are shown in Fig. 3 for the different integration times of 10 ms, 100 ms, 1 s, and 10 s. The average detection rate was 23 kHz and 34 Hz background per bin. To compare the traces, they were normalized with respect to the integration time. The two different fits used for the traces were either a single Gaussian or an exponentially modified Gaussian, given by
Fig. 3. Four lidar traces of the same diffusely reflecting target. The integration time ranged from 10 s to 10 ms. The maximum number of detected signal photons per range bin in the integration times were 8300, 850, 96, and 14, respectively.
In Fig. 4, the standard deviation for the estimated position with the two different fits is shown for different integration times. The data comes from a single 100 s integration time lidar measurement subdivided into segments with a duration ranging from 10 ms to 1 s. The segments spanned the entire 100 s and did not overlap. The number of segments for the different durations therefore ranged from 10,000 to 100 for 10 ms and 1 s, respectively. The fits were applied to each segment. The standard deviation of the peak position of the fit for the different segments of the same duration was then calculated. The standard deviation decreased with longer integration time, as expected. It is evident in Figs. 3 and 4 that the exponentially modified Gaussian performed better than the Gaussian fit for longer integration times. However, Fig. 4 clearly indicates that the Gaussian fit was superior for determining the range to the target for shorter integration times. It is possible to apply more advanced methods and fit other functions that better describe the response of the SPAD [27]. However, the scope of this work was not to find the best fitting method; hence, a Gaussian fit was deemed sufficient and used for the 3D imaging. With 10 ms integration time, corresponding to 10,000 laser pulses, and on average 226 detected photons in the target reflection peak, the standard deviation was still below 1 mm for the Gaussian fit. The temporal width (FWHM) of a single trace with an integration time of 1 s was 76 ps, which corresponds to roughly 11 mm. This temporal width was limited by the detector and jitter associated with the laser synchronization signal and gating. The upconversion stage adds a small jitter since the upconversion might happen at any point in the nonlinear crystal and dispersion causes the upconverted pulse to travel slower than the 3 µm pulse. The maximum jitter induced by this can be estimated as ${L_{\rm{QPM}}} \cdot {\rm GVM}$, where ${L_{\rm{QPM}}}$ is the length of the grating in the upconversion crystal, and GVM is the group velocity mismatch between the 3 µm and upconverted light. With the crystal used in this experiment, this jitter is 1 ps and hence not limiting the resolution.
Fig. 4. Standard deviation for the peak position of the different fits versus integration times. EMG refers to the exponentially modified Gaussian.
Fig. 5. Object used for the 3D scan is depicted in (a) with the corresponding measured lidar scan in (b). (c) Data and corresponding fit along the dashed line in (b).
The 3D printed object used for verification of the imaging capability, together with its image, can be seen in Fig. 5, along with the associated 3D lidar scan, ${{20}} \times {{20}}$ pixels over the $25 \;{\rm{mm}} \times {{25}}\;{\rm{mm}}$ area. The system was able to distinguish the 1 mm deep indentation in the object with an integration time of 1 s. This is in agreement with the standard deviation of 0.2 mm for 1 s integration time, as shown in Fig. 4. The red line in Fig. 5(b) is the horizontal line for which the lidar trace can be seen in Fig. 5(c). A square pulse was fitted to the data and yielded an estimated depth of 1.03 mm and a width of 9 mm for the indentation. The estimated depth of the indentation is well within the error margin for the indentation, originating from the 3D print and the glue under the paper. The estimated width of the indentation was 0.6 mm, too small compared with a caliper measurement; however, since each pixel was separated by 1.25 mm, this is reasonable. The spot size of the laser on the target was significantly smaller than the pixel size. There is a signal missing from some pixels in the scan of the object. The reason the signal for these pixels was too low to detect the surface is likely speckle, since the 1 m object distance and 1 in. lens would give high speckle contrast [28]. The speckle also caused the number of detected signal photons to vary for the pixels with a detected range. Despite the same integration time of 1 s, the number of detected signal photons per pixel varied from 500 to 30,000, as shown in Fig. 6. The rate of detected signal photons did not show any significant fluctuations over time for individual pixels. This indicates that the fluctuations were not caused by power fluctuations.
The estimated bandwidth (90 nm) of the idler is comparable with the acceptance bandwidth (80 nm) of the upconversion signal for the 300 fs pulses. It would be advantageous to use pulses of slightly longer duration to reduce the bandwidth of the lidar pulses, to match the acceptance bandwidth. It is also important to consider the bandwidth of the upconverted light. If the bandwidth of the mixing laser is considered narrow with respect to the MIR bandwidth, then the frequency bandwidth of the upconverted light can be approximated as the frequency bandwidth of the MIR light. In that case, the following relation will follow:
4. CONCLUSION
In this work, a photon-counting lidar above 3 µm wavelength is presented. It is based on upconverting the MIR radiation to 790 nm through intracavity SFG in a continuous-wave 1064 nm laser and detecting the upconverted light. By demonstrating photon-counting lidar above 3 µm, it shows that upconversion photon counting is a good technique for differential absorption lidar in the atmospheric transmission window where many gases have characteristic features [29]. Through TCSPC, the system was able to resolve 1 mm deep features. Furthermore, sub-mm standard deviation for the estimated position was obtained with as low as 10 ms integration time. By utilizing intracavity power, the required diode pump power to reach the same pump intensity for upconversion in the nonlinear crystal is reduced by more than one order of magnitude. This reduced the thermal load on the upconversion detection system (especially on the ${\rm{Nd}}{:}{{\rm{YVO}}_4}$ crystal), which, in combination with silicon detectors, removes the need for active cooling in the whole detection system. Furthermore, by exploiting QPM, it is possible to freely choose the wavelength to upconvert by using the appropriate grating period for the nonlinear crystal. This implies that the method could be used to perform TCSPC lidar at any wavelength where the nonlinear crystal is transparent. The standard QPM crystals KTP and lithium niobate remain transparent up to 4 and 5 µm, respectively [30,31]. A promising future material for reaching out further in the IR is orientation-patterned gallium phosphide, which is transparent up to 12 µm [32].
Funding
Totalförsvarets Forskningsinstitut; Vetenskapsrådet.
Acknowledgment
The authors would like to thank Dr. Robert Lindberg for rebuilding the tuneable fiber laser used as the seed for the OPA, and Dr. Andrius Zukauskas and Prof. Carlota Canalias for providing the PPKTA for the OPA.
Disclosures
The authors have no conflicts to disclose.
Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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