This disclosure relates generally to frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR), more particularly, to solid state FMCW LiDAR systems.
A reference interferometer is used by conventional frequency FMCW LiDARs to help characterize and correct for non-linearity in laser chirp. Conventional lidar or optical coherence tomography (OCT) systems rely on a reference interferometer with balanced photodiode to estimate laser frequency indirectly and calibrate any measurement data. In order to ensure accuracy in the phase extraction from a reference interferometer and a balanced photodiode, the reference interferometer typically needs to have long delay lines, or short delay lines and careful bias control—i.e., it would be off chip. Moreover, long delay lines are typically a challenging problem for integrated photonics, due to the high losses incurred in small waveguides. In addition, performance of FMCW lasers may drift on various timescales, over the course of several measurements with temperature or other environmental conditions, or over the course of the lifetime of the FMCW sensor.
Moreover, conventional FMCW LiDAR systems use mechanical moving parts and bulk optical lens elements (i.e., a refractive lens system) to steer the laser beam in two directions. And for many applications (e.g., automotive) are too bulky, costly, and unreliable.
A LiDAR chip of a solid state FMCW LiDAR system. The LiDAR chip includes an optical switch network, a switchable coherent pixel array (SCPA), and a monitoring assembly. The optical switch network is on the LiDAR chip. The optical switch network is configured to selectively provide coherent light to one or more of a plurality of output waveguides. The SCPA is on the LiDAR chip. The SCPA includes coherent pixels (CPs), and each of the CPs is configured to emit coherent light provided by a corresponding output waveguide of the plurality of output waveguides. The monitoring assembly is on the LiDAR chip. The monitoring assembly includes a plurality of photodetectors, and each of the plurality of photodetectors is configured to generate an output signal responsive to a level of light detected from a corresponding output waveguide of the plurality of output waveguides. The optical switch network is calibrated (e.g., by a controller) by adjusting a drive strength of switch drivers for the optical switch network based on output signals from the monitoring assembly.
In some embodiments, the LiDAR chip includes a splitter, an interferometer, an optical switch network, and a SCPA. The splitter is on the LiDAR chip. The splitter is configured to split coherent light into a first portion and a second portion. The coherent light is chirped according to a waveform. The interferometer is on the LiDAR chip. The interferometer is configured to generate an in-phase (I) signal and a quadrature (Q) signal using the first portion of the coherent light. The optical switch network is on the LiDAR chip. The optical switch network is configured to selectively provide the second portion of the coherent light to one or more of a plurality of output waveguides. The SCPA is on the LiDAR chip. The SCPA includes coherent pixels (CPs), and each of the CPs is configured to emit coherent light provided by a corresponding output waveguide of the plurality of output waveguides. A controller is configured to identify deviations in frequency of the coherent light based in part on the I and Q signals, and control a shape of the waveform based in part on to compensate for the identified deviations. Note that in some embodiments, the LiDAR chip may also include the monitoring assembly as described in the previous paragraph.
In some embodiments, the LiDAR chip is part of a focal plane array (FPA) system of a solid state FMCW LiDAR system. The FPA system includes a splitter, an interferometer, an optical switch network, a SCPA, a monitoring assembly, and a lens system. The splitter is on the LiDAR chip. The splitter is configured to split coherent light into a first portion and a second portion, and the coherent light is chirped according to a waveform. The interferometer is on the LiDAR chip. The interferometer is configured to generate an in-phase (I) signal and a quadrature (Q) signal using the first portion of the coherent light. The optical switch network is on the LiDAR chip. The optical switch network is configured to selectively provide the second portion of the coherent light to one or more of a plurality of output waveguides. The SCPA is on the LiDAR chip. The SCPA includes coherent pixels (CPs), and each of the CPs is configured to emit coherent light provided by a corresponding output waveguide of the plurality of output waveguides. The monitoring assembly is on the LiDAR chip. The monitoring assembly includes a plurality of photodetectors, and each of the plurality of photodetectors is configured to generate an output signal responsive to a level of light detected from a corresponding output waveguide of the plurality of output waveguides. The lens system is positioned to direct coherent light emitted from the SCPA into an environment as one or more light beams, and each of the one or more light beams is emitted at a specific angle and the specific angle is based in part on positions of the CPs on the LiDAR chip that generated the coherent light that form the one or more beams. A controller is configured to identify deviations in frequency of the coherent light based in part on the I and Q signals, and control a shape of the waveform based in part on to compensate for the identified deviations. The controller is also configured to calibrate the optical switch network based on output signals from the monitoring assembly.
Embodiments of the disclosure have other advantages and features which will be more readily apparent from the following detailed description and the appended claims, when taken in conjunction with the examples in the accompanying drawings, in which:
A solid state FMCW LiDAR system determines depth information (e.g., distance, velocity, acceleration, for one or more objects) for a field of view of the system. The solid state FMCW LiDAR directly measures range and velocity of an object by directing a frequency modulated, collimated light beam into a local area. The light that is reflected from an object within the local area, Signal, is mixed with a tapped version of the beam, referred to as the local oscillator (LO). The frequency of the resulting radiofrequency (RF) beat signal is proportional to the distance of the object from the solid state FMCW LiDAR system once corrected for the doppler shift that requires an additional measurement. The two measurements, which may or may not be performed at the same time, provide range and velocity information of the target.
The solid state FMCW LiDAR system uses on-chip monitoring and calibration circuits for achieving high-performance solid-state beam steering and laser chirping. The solid state FMCW LiDAR system include a focal plane array (FPA) system. The FPA includes one or more switchable coherent pixel array (SCPAs). The one or more SCPAs may be positioned at a focal plane of a lens system, such that the FPA system can perform solid-state beam steering for a single dimension and/or two dimensions. The direction of the incoming beam is mapped into a discrete position of a focused spot, and vice versa. One challenge for an SCPA is to maintain optimal calibration settings for the switch network to achieve low insertion loss, high extinction ratio and low crosstalk at any time. The solid state FMCW LiDAR system uses an on-chip feedback mechanism to enable in situ calibration or real-time closed-loop control of high-performance solid-state beam steering.
The on-chip feedback mechanism facilitates maintaining a high-quality laser chirp by the solid state FMCW LiDAR, which senses range by measuring interference between optical signals from a local path and a target path. By sweeping a frequency of a laser, the interference signal becomes an oscillation with a frequency proportional to target distance. FMCW lasers are modulated to have a linear frequency sweep from lower frequency to higher frequency, and then from higher frequency to lower frequency, in a triangular fashion. Often, lasers tuned in this fashion must be tuned with a particular drive signal or the frequency sweeps can deviate significantly from linear. Linearity deviations cause significant inaccuracies in range and velocity measurements derived using the FMCW LiDAR.
In some embodiments, the solid state FMCW LiDAR system utilizes on-chip monitoring and calibration circuits for solid-state beam steering realized by one or more integrated SCPAs. The solid state FMCW LiDAR system uses on-chip optical power monitoring circuits to enable in situ calibration or real-time closed-loop control of the optical switch network. For example, the LiDAR chip includes (i.e., on-chip) an optical switch network, a switchable coherent pixel array (SCPA), and a monitoring assembly. The optical switch network is configured to selectively provide coherent light to one or more of a plurality of output waveguides. The SCPA includes coherent pixels (CPs), and each of the CPs is configured to emit coherent light provided by a corresponding output waveguide of the plurality of output waveguides. The monitoring assembly includes a plurality of photodetectors, and each of the plurality of photodetectors is configured to generate an output signal responsive to a level of light detected from a corresponding output waveguide of the plurality of output waveguides. The optical switch network is calibrated (e.g., by a controller) by adjusting a drive strength of switch drivers for the optical switch network based on output signals from the monitoring assembly.
The one or more SCPAs are placed at a focal plane of a lens system for fast solid-state beam steering and co-axial FMCW LiDAR operation. On-chip optical monitoring circuits with optical couplers and monitoring photodetectors (PD) monitor the optical power at the output ports of an optical switch network. With this design, in situ calibration and real-time closed-loop control can be performed without affecting the coherent pixels or interrupting the normal operation of the LiDAR. For large-scale or multi-channel switchable coherent pixel arrays, a crossbar-type or a hierarchical (e.g., a binary tree) connection scheme can be used to read the signal from any arbitrary monitoring PD with significantly reduced number of I/Os and receivers for the monitoring circuits.
In some embodiments, the solid state FMCW LiDAR system utilizes on-chip monitoring and calibration circuits for generating a high-quality laser chirp signal. For example, the LiDAR chip may include (i.e., on-chip) a splitter, an interferometer, an optical switch network, and a SCPA. The splitter is configured to split coherent light into a first portion and a second portion. The coherent light is chirped according to a waveform. The interferometer is configured to generate an in-phase (I) signal and a quadrature (Q) signal using the first portion of the coherent light. The optical switch network is configured to selectively provide the second portion of the coherent light to one or more of a plurality of output waveguides. The SCPA includes coherent pixels (CPs), and each of the CPs is configured to emit coherent light provided by a corresponding output waveguide of the plurality of output waveguides. A controller is configured to identify deviations in frequency of the coherent light based in part on the I and Q signals, and control a shape of the waveform to compensate for the identified deviations. Note that in some embodiments, the LiDAR chip may also include the monitoring assembly as described in the previous paragraph.
The solid state FMCW LiDAR system uses a swept-source laser and a frequency-discrimination interferometer with optical hybrid for laser driver calibration. In some embodiments, a programmable laser driver (current or voltage source) directly drives a tuning laser source to create alternating positive and negative frequency sweeps. In other embodiments, a programmable modulator driver directly drives a modulator to induce positive and negative frequency sweeps on the seed laser beam. This is followed by the interferometer that includes a splitter, which sends light down two paths, one “local” path and one “reference” path, an optical combiner known as an “90-degree optical hybrid,” a photoreceiver with multiple photodetectors, and a controller for signal processing. The controller calculates an instantaneous signal phase and laser frequency using the outputs from the optical hybrid. The resulting instantaneous laser frequency is fed back to the drive signal generator to compensate for deviations in laser frequency from linear. In addition, the interferometer and optical hybrid can be used to calibrate for any non-linearity that results from residual error in the predistortion process or from laser/environmental drift. Accordingly, the solid state FMCW LiDAR system performs in situ generation of laser driver signals, and in situ calibration of residual non-linearity and laser performance drifts.
Note that in some embodiments, both the monitoring assembly for calibration of the optical switch network, and the interferometer with optical hybrid for laser driver calibration can be implemented on the same LiDAR chip. As such the LiDAR chip would be able to not only calibrate the optical switch network but also calibrate the laser driver.
As noted above, conventional LiDAR systems that use an interferometer to help characterize and correct for non-linearity in laser chirps have long delay lines or short delay lines and careful bias control. Long delay lines are problematic for integrated photonics, due to the high losses incurred in small waveguides, and may be off-chip. Likewise careful bias control generally correlates with an increase in complexity of control circuitry. In contrast, the solid state FMCW LiDAR system performs laser frequency measurement using on-chip short delay lines without complex bias controls. Moreover, the solid state FMCW LiDAR system is configured to measure laser frequency and dynamically adjust the drive waveform of the laser to account for changes of the laser characteristics over time or over environmental conditions.
Note that the LiDAR chip can steer the light emitted from the solid state LiDAR system in at least a first angular dimension (e.g., elevation). And the solid state FMCW LiDAR system may include, e.g., a scanning mirror (e.g., moving mirror, polygon mirror, etc.) to steer the light in a different angular dimension (e.g., azimuth). And in some embodiments, optical antennas within the one or more SCPAs are arranged in two-dimensions such that the LiDAR chip can steer the optical beam two-dimensions (e.g., azimuth and elevation). Being able to steer the beam without moving parts may mitigate form factor, cost, and reliability issues found in many conventional mechanically driven LiDAR systems.
The SCPA 115 includes coherent pixels, and each of the coherent pixels is configured to emit coherent light provided by a corresponding output waveguide of the plurality of output waveguides. Each coherent pixel includes an optical antenna 105 for emitting and receiving optical signals and other passive and active optical components such as waveguides, couplers, hybrids, gratings and photodetectors for generating the RF signals. The SCPA 115 is placed at a focal plane of a lens system 107. The lens system 107 includes one or more optical elements (e.g., positive lens, freeform lens, Fresnel lens, etc.) which map a physical location of each coherent pixel, to a unique direction. In some embodiments, the lens system 107 is positioned to collimate the transmitted signals emitted via the plurality of optical antennas 105. The lens system 107 is configured to project a transmitted signal emitted from an optical antenna of the plurality of antennas into a corresponding portion of a field of view of the FPA system, and to provide a reflection of the transmitted signal to the optical antenna. Each optical antenna sends and receives light from a different angle. Therefore by switching to different antennas, a discrete optical beam scanning is achieved. The FPA system scans a laser beam 108 across targets in the field-of-view of the FPA system, and the coherent pixels in the FPA system generate electrical signals which are then digitally processed to create LIDAR point clouds. The lens system 107 produces collimated transmitted signals that scan the transceiver field of view along one or more angular dimensions (e.g., perform a 2-D scan of the local area). Note that by switching the light to different coherent pixels, the LiDAR chip 106 emits or receives collimated laser beam 108 at different angles, enabling discrete solid-state scanning and co-axial optical sensing for a single channel or multiple channels in parallel.
Electrical signals from the one or more monitoring circuits 200 are then processed via a receiver 206. The receiver may include, e.g., an amplifier, integrator, switches, etc. Data output from the receiver 206 is quantized by an analog-to-digital converter (ADC) 207. The output of the ADC 207 is then processed in a controller 208. The controller 207 may include, e.g., control circuits, computer processor, field-programmable gate array (FPGA), digital signal processor, microcontroller, application specific integrated circuit (ASIC), or some combination thereof. As illustrated the receiver 206, the ADC 207, the controller 208, and the switch driver 209 are separate from the LiDAR chip 106. In other embodiments, some or all of the receiver 206, the ADC 207, the controller 208, and the switch driver 209 may be also be integrated into the LiDAR chip 106. Additionally, while a single receiver 206, and a single ADC 207 are shown, in some embodiments, there may be a plurality of receivers 206 and a corresponding plurality of ADCs. For example there may be a separate receiver 206 and a separate corresponding ADC 207 for each monitoring circuit.
Closed-loop calibration and/or control are done by adjusting a drive strength of switch drivers 209 for the optical switch network 103 based on the outputs of monitoring circuits. The calibration of the optical switch network 103 helps ensure light is passed to one or more target CPs using minimal power, and mitigates light being passed to non-target CPs. Note that the calibration of the optical switch network 103 occurs within the solid state FMCW LiDAR system and no external equipment is needed. For a large-scale or multi-channel switchable coherent pixel array, there could be hundreds of coherent pixels, hundreds of switch ports and therefore hundreds of monitoring PDs. As such, in some instances it may become impractical to assign individual electrical I/O pads/traces to each monitoring PD due to electrical I/O constraints.
The crossbar-type connection scheme is independent of the polarity of the monitoring PDs. In this example, cathodes of the monitoring PDs with the same row numbers are connected to form corresponding signal groups (also referred to as nodes) and anodes of monitoring PDs with a same channel value are connected to form corresponding bias groups. For example, as illustrated there are n channels, and cathodes associated with row N are connected to form a corresponding signal group 303. As such there are N signal groups. Similarly, anodes of monitoring PDs with a same channel value are connected to form corresponding signal groups (also referred to as nodes), as such there are n signal groups. For example, as illustrated the anodes of the monitoring PDs of channel 1 are connected to form a corresponding signal group 302.
Any monitoring PD can be selected for readout by selecting the corresponding pixel and row numbers on one or two analog multiplexers (MUX)—e.g., multiplexer (MUX) 304 and MUX 306. The MUX 304 and/or the MUX 306 may be controlled by the controller 208. For example, the controller 208 may configured the MUX 304 and/or the MUX 306 to read out one or more of the monitoring PDs. The MUX 304 and 306 (e.g., switches) can be implemented on the same LiDAR chip 106 or outside the LiDAR chip 106. For example, the output of MUX 306 can be connected to a constant bias voltage 307 to provide a reverse bias for the monitoring PDs and the signal groups (e.g., the signal group 302) can be used for outputting current signals. To read current from PD_k_j, the FPA system opens all switches except switch “j” and process the signal from the kth monitoring PD output. In this example, when switch 3 of the MUX 306 is on and the rest of the switches are off, only the third monitoring PD in each channel is activated. The monitoring PDs outputs can further be multiplexed to reduce the number of receiver channels with the MUX 304. This scheme enables independent optical power monitoring at all the ports of the entire switch network without assigning individual electrical I/O traces/pads to each monitoring PD. To avoid any leakage current from unselected monitoring PDs from other active channels, it is preferred to keep one channel active during monitoring and calibration process, which can be achieved by turning off laser sources or laser amplifiers for the other channels where no monitoring PD is selected. The monitoring and calibration processes for this scheme can happen during power-on or frame transitions.
To reduce the number of electrical I/Os, the PD bias 406 and PD output signals may be connected together. For each level in the binary tree, outputs of monitoring PDs with odd indices are connected together to form a first signal and outputs of monitoring PDs with even indices are connected together to form a second signal. For a 1-to-8 switch with 14 hierarchical monitoring PDs, a total number of electrical I/Os and corresponding receivers 405 for optical power monitoring are reduced from 14 to 6. More generally, for a 1-to-2N switch, the number of monitoring I/Os and receivers are reduced from 2N+1 to 2N, which is more significant as N grows.
For an example, to calibrate switch settings to direct all the light into coherent pixel P2. The controller 208 starts with reading monitoring signals from receiver L0 and H0 and optimizing control signals for SW0 that maximize L0 reading and minimize H0 reading. The controller (e.g., the controller 208) then moves to the next stage and optimizes control signals for SW1_0 that maximize H1 and minimize L1. For the last stage SW2_1, the controller attempts to maximize L2 and minimize H2. This hierarchical calibration process minimizes leakage and crosstalk from unselected photodetectors. It is also electrically decoupled from the electrically sensitive coherent pixel cells which requires low-noise and high-speed operation for FMCW LiDAR. This enables in situ calibration and real-time closed-loop control without affecting the coherent pixels or interrupting the normal operation of the LiDAR.
The solid state FMCW LiDAR system loads 605 a driving waveform. For example, a microcomputer of a LiDAR processing engine of the solid state FMCW LiDAR system may load the driving waveform. The driving waveform may be a generic or previously stored driving waveform.
The solid state FMCW LiDAR system frequency modulates 610 a laser source with the loaded driving waveform. The modulated light forms one or more laser chirps. The frequency modulation may be performed by a laser controller that modulates a K-channel laser array in accordance with instructions from a LiDAR processing engine.
The solid state FMCW LiDAR system measures 615 the one or more laser chirps to form I and Q signals. For example, the solid state FMCW LiDAR system may measure the one or more laser chirps using optical hybrid photodetectors to generate the I/Q signals as shown and described above with regard to, e.g.,
The solid state FMCW LiDAR system processes 620 the I and Q signals. For example, the solid state FMCW LiDAR system may filter and/or sample the I and Q signals. The solid state FMCW LiDAR system may process the I and Q signals using a LiDAR processing engine.
The solid state FMCW LiDAR system determines 625 phases of the processed I and Q signals. The solid state FMCW LiDAR system may determine the phases of the processed I and Q signals using the LiDAR processing engine. Phase may be determined by, e.g., calculating an arc-tangent of a quotient of the processed I and Q signals. This is equivalent to measuring the phase angle of a signal created by adding the I-channel to the Q-channel modified by multiplying the Q-channel by the imaginary number i.
The solid state FMCW LiDAR system determines 630 an instantaneous frequency of the laser using the phases. The instantaneous frequency of the laser may be determined by, e.g., dividing the phase calculated in the previous step by an optical path time delay of a delay arm (e.g., the delay arm 502).
The solid state FMCW LiDAR system controls 635 the drive waveform of the laser source based in part on the instantaneous frequency to generate a modified output beam. The solid state FMCW LiDAR system monitors a strength of deviations in the instantaneous frequency of the laser at different time instances. Based on the strength of the deviations, the solid state FMCW LiDAR system adjusts the driving waveform (e.g., adjusts a shape of the driving waveform) to compensate for slower or faster chirp rates. Such adjustment can be done at once by updating pre-loaded laser model and adapting drive waveform through analytical solutions, or iteratively by tuning the parameterized drive waveform through gradient descent optimization algorithms. The controlled drive waveform is then re-applied to the laser source to generate a modified output beam. Note that steps 615-635 may be iterative and loop one or more times in performing the calibration.
The solid state FMCW LiDAR system collects 640 FMCW measurements using the modified output beam. The solid state FMCW LiDAR system scans (e.g., via the FPA system) the modified output beam across a local area, and measures reflections of the modified output beam from one or more objects in the local area to generate the FMCW measurements.
The solid state FMCW LiDAR system determines 645 range and/or velocity data using the FMCW measurements. The solid state FMCW LiDAR system estimates range and velocity data using the FMCW measurements based on an expected chirp rate of the laser. If residual deviations from linear still exist, they are measured by the same process 605-630 and used to adjust the calculation of range and/velocity data. This process results in more accurate point clouds.
The laser controller 720 receives control signals from a LiDAR processing engine 725, via a digital to analog converter 730. The processing also controls the FPA driver 710 and sends and receives data from the LiDAR chip 106.
The LiDAR processing engine 725 includes a microcomputer 735. The microcomputer 735 processes data coming from the FPA system and sends control signals to the FPA system via the FPA driver 710 and laser controller 720. Note that the microcomputer 735 may include the controller 208 and/or the controller 511. The LiDAR processing engine 725 also includes a N-channel receiver 740. Signals are received by the N-channel receiver 740, and the signals are digitized using a set of M-channel analog to digital converters (ADC) 745.
Note that the LiDAR chip 106 can steer the light emitted from the solid state LiDAR system over one or more angular dimensions. In some embodiments, the LiDAR chip 106 is configured to steer the beam only over a first angular dimension (e.g., elevation). The FPA system 705 may include one or more scanning mirrors (not shown) that can steer the optical beam in a second dimension (e.g., orthogonal to the first angular dimension—e.g., azimuth). The scanning mirror receives light from the lens system 702 and directs it into the target area along a particular angular field of view determined by the first angular dimension (controlled by the LiDAR chip 106) and the second angular dimension (controlled by the one or more scanning mirrors). Note that the above example of use of one or more scanning mirrors is in the context of the LiDAR chip 106 being configured to scan only over a first angular dimension. However, in some embodiments, the one or more scanning mirrors may be used with a LiDAR chip 106 that is configured to scan in a plurality of angular dimensions (e.g., azimuth and elevation). For example, with a two dimensional arrangement of the optical antennas (e.g., rectangular grid) signals from the plurality of optical antennas may be scanned in two dimensions within the field of view of the one or more scanning mirrors.
The figures and the preceding description relate to preferred embodiments by way of illustration only. It should be noted that from the preceding discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of what is claimed.
Although the detailed description contains many specifics, these should not be construed as limiting the scope of the invention but merely as illustrating different examples. It should be appreciated that the scope of the disclosure includes other embodiments not discussed in detail above. Various other modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope as defined in the appended claims. Therefore, the scope of the invention should be determined by the appended claims and their legal equivalents.
Alternate embodiments are implemented in computer hardware, firmware, software, and/or combinations thereof. Implementations can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions by operating on input data and generating output. Embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. Each computer program can be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language can be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Generally, a computer will include one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM disks. Any of the foregoing can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits) and other forms of hardware.
This application is a continuation of International Application No. PCT/US2021/014519 filed Jan. 22, 2021, which claims the benefit of and priority to two U.S. Provisional Applications including U.S. Provisional Application No. 62/966,983 filed Jan. 28, 2020, and U.S. Provisional Application No. 62/965,094 filed Jan. 23, 2020. The entire disclosures of International Application No. PCT/US2021/014519 and U.S. Provisional Patent Applications 62/966,983 and 62/965,094 are hereby incorporated by reference as if fully set forth herein.
Number | Date | Country | |
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62966983 | Jan 2020 | US | |
62965094 | Jan 2020 | US |
Number | Date | Country | |
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Parent | PCT/US2021/014519 | Jan 2021 | US |
Child | 17869407 | US |