A thin phase mask is added at the lens aperture. The rest of the optics stays as it is.
2Dimage
Each point of light reaches the sensor as a shape that depends on its depth.
3Dmeasurement
Reconstruction software that knows the mask reads every shape back into a position in space.
nearerfarther
Phase mask
Fig. 1. Simulation for illustration: tracer particles in a swirling flow, imaged through a saddle-shaped phase mask.
123D is a computational imaging design house. We design and fabricate one optical element and pair it with matched reconstruction software, so that a single camera measures what usually takes several cameras, a scanner or a laser.
The methods come from Prof. Yoav Shechtman’s lab at the Technion, where they have been shown from localization at tens of nanometers under a microscope to passive ranging at distances of up to 5 km.
A lens images a point of light as a small spot. When the point moves out of focus the spot grows, and it grows the same way whether the point moved nearer or farther. An ordinary image therefore says little about depth.
A phase mask is a transparent plate with a micro-structured surface. Placed at the lens aperture, it reshapes the spot so that its form changes steadily with distance. In the example below, the two lobes separate in one direction in front of focus and in the perpendicular direction behind it.
Because the mask is designed, the code is known. Reconstruction software reads each spot and returns its position in three dimensions, including where spots overlap.6 The same approach serves other purposes too, such as extending the depth of field or encoding color.
Standard lens
With phase mask
nearerin focusfarther
Fig. 2. The image of one point of light at nine depths. Simulated by scalar diffraction; masks for real systems are optimized for each instrument.
Where we apply it
The same principle works at very different scales. We are active in four areas and are opening a fifth.
Fig. 3. Size of the scene in each area, on a logarithmic scale. Solid: demonstrated. Dashed: expected or simulated, not yet demonstrated. An arrow means the range continues. Ranges are indicative.
Microscopy and inspection
Depth and extended focus from a single snapshot
In a microscope the mask sits at the aperture of the objective. One snapshot then gives depth, an extended depth of field or a surface height map, without scanning through focus.3,4
Extended depth of field, up to about 3×
Single-shot autofocus
Surface profiling and 3D defect detection
Volumetric imaging in transparent media
Spectral encoding with a diffractive element
For microscope makers and for inspection in semiconductors, electronics and precision manufacturing. Published demonstrations so far are in biological microscopy and surface metrology.
Volumetric particle image velocimetry normally triangulates between several cameras, typically four to six. Our method, PET-PIV, adds one thin phase mask to an otherwise conventional PIV setup and recovers the three-dimensional velocity field by deep learning. Benchmarked against a four-camera tomographic system, its velocity magnitude correlated with the reference at about 0.97 to 0.98.1
One optical path into the flow
No alignment, calibration or synchronization between cameras
Works at high seeding density, where particle images overlap
Processing about 30× faster than the tomographic reference, on the way to real-time monocular PIV
Passive depth from an existing camera or telescope
An add-on phase plate and matched software turn one camera into a passive depth sensor. There is no laser, no second camera and no emitted signal. The method is published for kilometer-scale ranging through a telescope.2 It has been demonstrated at distances of up to 5 km, and simulations show that 10 km and more are within reach.
Passive: nothing is emitted
One thin, light plate on the existing optics
Works at long range, where a stereo baseline becomes impractical
For drones, robotics, navigation, photogrammetry, infrastructure and security.
123D’s optics fabrication service, also at rapidopticsfab.com
Diffractive optical elements normally need nanometer-scale precision and cleanroom lithography. Near-index matching forms the element at the boundary between two materials with almost the same refractive index. The features can then be hundreds of times larger, and a commercial 3D printer can make them.5
Phase masks, diffractive elements, Fresnel lenses and aberration-correcting optics
Fast iteration for prototypes and proofs of concept
Large elements that are hard or expensive to make by lithography
For optical designers and instrument makers who need a custom element quickly. It is also how we make the phase masks for our own projects.
We believe the same approach extends to bubbles: where they form, how large they are, and how they move, merge and detach, together with the liquid flow around them. We have not demonstrated this on bubbles yet.
We are looking for design partners in boiling and two-phase cooling, cavitation and related fields.
How we work
We work as a design house, around a partner’s own instrument and measurement problem. Projects take one of two forms.
Optical elements
Design and rapid prototyping of phase masks and diffractive elements to your specification.
Integrated imaging projects
A phase mask designed for your instrument, together with the reconstruction software matched to it.
First conversationNon-confidential. You describe what you measure today and what limits it. We present our results and say whether we see a fit.
FeasibilityWe design a mask for your optics and agree what a demonstration needs to show.
DemonstratorWe fabricate the element, adapt the reconstruction to your setup and test it against your reference.
IntegrationThe element and software are fitted into your system.
We are looking for a small number of design partners for focused, funded first projects.
Who we are
123D commercializes computational imaging methods developed in Prof. Yoav Shechtman’s lab at the Technion – Israel Institute of Technology. The group has about fifteen years of experience in point-spread-function engineering and computational imaging.
Yotam Zohar
Co-founder and CEO
Talpiot graduate with an M.Sc. in physics from the Technion. Previously founder and CEO of Cogito Systems / AlGoals, and Chief Data Officer at Sayata (acquired by Global Indemnity Group, NYSE: GBLI). At 123D he leads commercialization, strategy and partnerships.
Prof. Yoav Shechtman
Co-founder and CSO
Professor at the Technion. His work covers computational imaging, point-spread-function engineering, microscopy, phase-mask and diffractive optics, optical design by deep learning, and additive fabrication of optics. Recipient of the IUPAB Young Investigator Medal and the IAMBE Early Career Award.
Published work
D. Xiao et al., “High-density monocular 3D particle image velocimetry by wavefront shaping and deep learning,” arXiv:2607.29163 (2026).
N. Opatovski et al., “Monocular kilometer-scale passive ranging by point-spread function engineering,” Optics Express 30, 37925 (2022).
N. Opatovski et al., “Depth-enhanced high-throughput microscopy by compact PSF engineering,” Nature Communications 15, 4861 (2024).
R. Gordon-Soffer et al., “Microscopic scan-free surface profiling over extended axial ranges by point-spread-function engineering,” Science Advances 6, eabc0332 (2020).
R. Orange Kedem et al., “Near index matching enables solid diffractive optical element fabrication via additive manufacturing,” Light: Science & Applications 12, 222 (2023).
E. Nehme et al., “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nature Methods 17, 734 (2020).
R. Orange-Kedem et al., “3D printable diffractive optical elements by liquid immersion,” Nature Communications 12, 3067 (2021).
L. Leites et al., “Fabrication of customized diffractive optics in under 10 minutes via single-shot grayscale projection on a consumer-grade DLP system,” ACS Photonics (2026).
Talk to us
Tell us what you measure and what limits it today. A first conversation is non-confidential.
yotam@123dimaging.com
Yotam Zohar, co-founder and CEO. 123D is based in Israel.
PET-PIV is a monocular 3D PIV method: it measures a three-dimensional, three-component velocity field through a single camera. One thin phase mask is added to an otherwise conventional PIV setup, and deep-learning reconstruction recovers the flow, including at high seeding densities.
Status
Demonstrated in water, at laboratory scale and under a microscope
Benchmark
Four-camera tomographic PIV. Velocity magnitude correlates at about 0.97 to 0.98
Processing
About 30× faster than the tomographic reference: 1 s per frame against 30 s
Volumetric PIV usually triangulates between several cameras, typically four to six. Each camera adds cost, most of all at high speed, and needs its own view into the flow at a different angle. All of them have to be aligned, calibrated against each other to a fraction of a pixel and kept synchronized, and the tomographic reconstruction is slow.
Encoding depth through one camera removes the triangulation. The camera side of the experiment becomes a single optical path, with no alignment, calibration or synchronization between cameras to maintain.
Fig. 1a. Tomographic PIV: four cameras, four viewing angles, and the relationships between them.Fig. 1b.PET-PIV, short for point-spread-function-engineering, training-based PIV: one camera, one viewing angle, one added element.
Multi-camera tomographic PIV
PET-PIV
Cameras
Typically four to six
One
Views into the flow
Several, at different angles
One
Between cameras
Alignment, sub-pixel calibration, synchronization
Nothing to align or synchronize
Added optics
None
One phase mask at the lens aperture
Processing
About 30 s per frame in the published comparison
About 1 s per frame, or 30× faster
Still required
Illumination, seeding, optical access
The same
What you add to an existing setup
The only hardware change is one thin phase mask, placed at the iris of the camera lens or, in a microscope, beneath the objective. We design and fabricate it for your optical configuration and wavelength. Standard commercial lenses, cameras and suitable PIV lasers can otherwise be used.
Fig. 2. The Tetrapod phase mask of the published macro-scale experiments. Left: its designed phase pattern, 18 mm across. Right: the mask in its holder at the iris of a 100 mm macro lens. From Xiao et al. (2026), Fig. S2.
The camera’s depth response is then calibrated in place, from images of sparsely seeded flow and the known thickness of the illuminated volume.
From images to velocity fields
With the mask in place, each particle reaches the sensor as a shape that depends on its depth (Fig. 3). These shapes are larger than in-focus particle images and overlap as the seeding gets denser, which is why single-camera 3D methods have generally been limited to sparse seeding.
PET-PIV resolves the overlap computationally, with deep-learning code developed in Prof. Shechtman’s group. It works in two modes.
Fig. 3. A camera frame at 0.2 particles per mm³, with an enlarged detail. Below: the image of one particle at five depths across 20 mm. From Xiao et al. (2026), Fig. 3b.
Tracking mode
Locates every particle in three dimensions, links the positions into trajectories and interpolates them onto a grid. It returns particle positions, tracks and the velocity field. Its network is trained on synthetic images generated from the calibrated model of the optics, so it needs no multi-camera reference.
Tested at 0.05 to 0.2 particles per mm³
Field mode
For seeding too dense to separate individual particles. A second network maps five consecutive frames directly to the three-dimensional velocity field. It is currently trained with reference data from tomographic PIV.
Validated at about 0.4 particles per mm³
This is research software today, not a packaged product. Its processing is already about 30× faster than the tomographic reference, and we are heading toward real-time monocular PIV.
What has been shown
The published experiments measured swirling and jet flows in a water tank seeded with fluorescent tracer particles, at Reynolds numbers in the low thousands. A four-camera tomographic PIV system recorded the same flows at the same time and served as the reference (Fig. 4).
Fig. 4. The validation setup. The single PET-PIV camera (C0) stands among the four cameras of the tomographic reference system (C1 to C4). From Xiao et al. (2026), Fig. S2c.
Flow
Mode
Seeding density
Correlation with the reference
Swirl
Tracking
0.1 particles per mm³
About 0.98
Jet, Re 3000
Tracking
0.1 particles per mm³
About 0.97
Jet, Re 2000
Field
About 0.4 particles per mm³
About 0.98
Correlation coefficient of velocity magnitude between PET-PIV and the four-camera reference, averaged over 100 frames for the swirl and 300 frames for each jet. The reference was processed with Shake-The-Box for the tracking-mode comparisons and with tomographic PIV for the field-mode comparison. Values for each velocity component are in the paper’s supplement.
Under a microscope, the tracking mode measured steady and unsteady flows in microfluidic channels 20 to 100 µm deep, through 20× and 40× objectives.
Four of these measurements can be played in Fig. 5: the frames the single camera recorded, and what was measured from them.
Swirling flow. Tracking mode, 0.1 particles per mm³.
Camera frames
Velocity field: reference, PET-PIV, difference
Jet at Re 3000. Tracking mode, 0.1 particles per mm³.
Camera frames
Velocity field: reference, PET-PIV, difference
Jet at Re 2000. Field mode, about 0.4 particles per mm³, where particle images overlap too heavily to be located one by one.
Camera frames
Velocity field: reference, PET-PIV, difference
Microfluidic channel whose depth steps from 40 to 100 µm, through a 20× objective. Tracking mode.
Camera frames
Particle trajectories, colored by speed
Fig. 5. Four of the published measurements. In the tank flows, the rows are the three velocity components and the columns are the four-camera reference (labeled STB or TPIV), PET-PIV and the difference between them. From supplementary videos 1, 2, 6 and 8 to 12 of Xiao et al. (2026), CC BY 4.0, cropped, resized and trimmed.
Like any PIV measurement, it needs illumination, seeding and optical access to the measurement volume.
Demonstrations so far are in water, with fluorescent tracer particles. Measurements in air have not been shown yet.
Each optical configuration is calibrated in place before measuring.
The field mode is currently trained with reference data from tomographic PIV. Training on simulated data is the planned way to remove that step. The tracking mode does not need it.
We assess the measurement volume and acquisition conditions for each application.
How far it scales
The measurement volume is set by the optics, not by the method. The same depth encoding has been demonstrated from microscope scale to ranging at 5 km, and we expect flow measurement to be possible at scales of a kilometer and more. Seeding and reflections at those scales are a separate question.
Where it helps
PIV is used in wind tunnels, on propulsion systems and turbines, in microfluidics and in oceanic and atmospheric research. A single-camera architecture matters most where adding cameras is the hard part.
Limited optical access: one window or one viewing direction, as in turbomachinery or endoscopic configurations.
Test facilities where setup and calibration time is expensive, such as towing tanks and water tunnels.
Microfluidics, through a single microscope objective.
Existing planar PIV systems that need the third dimension with a minor modification.
Further out, one camera with fast decoding points from laboratory measurement toward monitoring the flow in running equipment: turbines, engines, combustion, cooling, pumps and compressors. That is the direction of development, not a present capability.
Working with us
We are looking for a small number of design partners for focused, funded first projects.
The first step is a short, non-confidential call. We present the technology and results, and you tell us which measurement constraints matter most in practice.
A thin phase plate added to an existing camera or telescope encodes distance into every frame, and matched software reads it back. Nothing is emitted and no second camera is needed. The method has been demonstrated at distances of up to 5 km.
Status
Demonstrated outdoors with a telescope-based research system
Range shown
About 200 m to 5 km. Simulations show 10 km and more
Most ways of measuring distance at range send out a signal. Lidar, time-of-flight cameras, structured light and gated imaging all carry an emitter, with its power, weight, eye-safety limits and cost, and they reach only as far as enough of their own light comes back.
Stereo is passive, but it needs two cameras held in calibration and a baseline that grows with the distance to be measured.
Encoding depth through a single aperture needs neither an emitter nor a baseline. The camera keeps its one optical path and uses the light the scene already provides (Fig. 1).
Working distance
10 cm1 m10 m100 m1 km10 km
Structured lightemits light
Time-of-flight camerasemit light
Stereopassive, two cameras
Gated imagingemits light
Lidaremits light
Imaging radaremits radio waves
Depth encodingpassive, one camera
Fig. 1. Typical working ranges of 3D sensing methods, on a logarithmic scale. The ranges are indicative of common systems; each method can be pushed further in special designs. Last row: solid, demonstrated; dashed, simulated.
Range is not the whole comparison. Active sensors measure distance directly and more precisely. What depth encoding offers is distance from a camera alone, at ranges where the other passive option stops.
What you add to an existing camera
The added hardware is a phase plate at an aperture plane of the optics: in front of the lens or telescope, or inside the instrument. The plate is thin, light and passive, and needs no power. The published demonstrator reached that plane with a lens relay behind a small telescope (Fig. 2).
We design the plate for the instrument: its aperture and focal length, its waveband, and the range of distances that matters. A plate can be designed for the camera’s band, from the visible to the mid-wave infrared. For large apertures the plate is large too, and we cast it by near-index matching, up to the size of a telescope aperture.
Fig. 2a. Optical layout of the published demonstrator, drawn in a straight line. A relay behind the telescope carries the phase mask at an aperture plane in front of the camera.1Fig. 2b. The instrument in a field test: a small telescope, the relay with the mask, a camera and a laptop.
How distance is read from the image
The plate used in the published work gives the optics a double-helix response: every point of light reaches the sensor as two spots, and the line joining them turns as the point’s distance changes (Fig. 3). The turn is fastest at short range and slows with distance, which is why precision is best close in.
240 m
473 m
1.1 km
2.1 km
5.5 km
10 km
Fig. 3. The image of one distant point through a double-helix plate, at six distances. The pair of spots turns by about half a turn between 240 m and 10 km.
A natural scene is not made of points, but each small region of it behaves the same way: it appears as two copies of itself, shifted along that line. The software finds the direction of the shift, either from the region’s cepstrum, a transform in which a repeated pattern shows up as a pair of peaks, or with a neural network. A calibration recorded once for the instrument turns the angle into a distance (Fig. 4).
Without the plate
Through the plate: every feature appears twice
Its cepstrum: the bright pair marks the direction of the shift
Fig. 4. A road sign about 1.75 km from the telescope. The cepstrum is shown only on the ring where the peaks of this plate can fall.
What has been shown
The published system was tested outdoors on moving targets, on static scenes with varied textures and in poor visibility, at distances beyond 1.7 km.1 Later measurements with the same approach reach 5 km.
Scene
Distances
Shown in
Car driving away
200 to 800 m
Fig. 5
Small drone flying away
About 380 m to 1.1 km
Fig. 5
Truck on a dusty road
About 300 to 700 m
Fig. 5
City scene, 18 regions
500 m to 1.7 km
Fig. 6a
City scene with GPS reference
About 350 m to 6 km
Fig. 6b
Scene in heavy dust
500 m to 1.2 km
Fig. 7
The car sequence and Figs. 6a and 7 are from the 2022 paper.1 The other measurements are from the group’s presentations.
A car drives away from the telescope, from about 200 m to 800 m. The white box marks the region being ranged; the two small panels show that region and its cepstrum with the detected direction. The plot adds one estimate per frame, by peak location (blue) and by neural network (green), on the reference distance (dashed).
A small drone flies away from the camera, from 377 m to about 1.1 km. The plate doubles its image, and the angle between the two copies changes with the distance, which is shown above the frame.
A truck drives away on a dusty road. The passive estimate (blue) follows it throughout. The laser rangefinder (red) agrees when it gets a return from the truck, and otherwise reads about 40 m or gives no reading.
Fig. 5. Three outdoor tests. The camera images of the car and the truck are shown in false color. The car sequence is supplementary movie S1 of Opatovski et al. (2022).1
Fig. 6a. Eighteen regions of a city scene, 500 m to 1.7 km away, and the distance estimated for each against ground truth. From Opatovski et al. (2022), panels A and B of the static-scene figure.1Fig. 6b. Later measurements: distance estimated from the image against GPS ground truth, out to 6 km. Circles are the points used for calibration, diamonds the test points.
Fig. 7. Ranging through heavy dust. D: the scene. E: the frame recorded through the plate. F: the distance estimated for each block of the frame, in meters. From Opatovski et al. (2022).1
What limits it
Precision is a fraction of the distance, and it loosens with range. Air turbulence is the main source of error far out: in the published system, 10% precision held to about 1.5 km in calm air and to about 500 m in strong turbulence.1
The region being ranged needs texture. Uniform surfaces are hard, and so are regions dominated by straight horizontal or vertical lines.
Aperture and field of view trade against precision. A larger aperture gives stronger depth cues; a wider field of view gives weaker ones.
The plate slightly lowers the sharpness of the ordinary image.
Results so far range selected objects and regions. A distance for every pixel, together with the restored sharp image, is the development step of a full project.
Computing load grows with the frame rate and with the number of regions ranged.
Where it helps
Passive single-camera ranging matters most where an emitter is unwelcome or too heavy, and where the distances are too long for stereo.
Long-range observation and site monitoring: distance to what a camera already sees, at hundreds of meters to kilometers.
Detecting and tracking small drones and other objects in the air.
Drones and ground robots, where the weight and power of a lidar are hard to carry.
Navigation and landing aids.
Surveying and mapping from a single camera.
Working with us
We are looking for integration partners who want to add passive depth to an imaging system they already build or operate.
A project starts from your optics and your range requirement. We design and make the plate, demonstrate ranging on agreed targets with your system, and then extend the software toward a full depth image.
N. Opatovski, D. Xiao, G. Harari and Y. Shechtman, “Monocular kilometer-scale passive ranging by point-spread function engineering,” Optics Express 30, 37925 (2022).
A microscope objective sees only a thin slice of the sample sharply, and its blur looks the same above focus and below. A phase mask at the aperture, with matched software, changes what one snapshot holds: the height of a surface, the depth of a defect, or an image that stays sharp through several times the usual depth of field. The aim is to measure and inspect without scanning through focus.
Status
Shown on research microscopes, in surface metrology and biological imaging
Height precision
About 30 nm over a 4 µm range, on a tilted silicon wafer
At high magnification the depth of field is about a micrometer or less. A sample that is tilted, warped, stepped or simply thicker than that has to be refocused or scanned slice by slice, and every slice costs acquisition time, data and stage motion.
Autofocus has the same root. A blurred image does not say which way focus lies, so the instrument has to search for it.
What the mask changes
A phase mask at the aperture plane of the objective reshapes the image of every point in the sample. The shape is designed for the job (Fig. 1).
Standard objective
Depth-encoding mask
Extended-focus mask
below focusin focusabove focus
Fig. 1. The image of one point at nine depths through focus, for three apertures. Simulated by scalar diffraction, for illustration; masks for real instruments are optimized for each objective.
Measure depth
A depth-encoding mask, such as the Tetrapod, gives each point a shape that changes steadily through focus. Software reads the shape as a height: for surface profiles, for the depth of a defect or particle, and for volumetric imaging of transparent samples.
Extend focus
An extended-focus mask does the opposite. It keeps the response nearly constant through focus, so that one processed image is sharp over up to about 3× the usual depth of field.
Find focus in one shot
An encoded shape shows both how far a surface is from focus and in which direction. One frame can then do the work of a focus sweep.
Encode color
A diffractive element can also write wavelength into the shape of the response, for spectral imaging through a single channel.4
What has been shown
Surface profiling without scanning
A pattern of light spots is projected onto a reflective surface, and the shape of each spot’s image gives the local height.1 On a silicon wafer tilted under a 100× objective, 70 spots mapped a height range of 4 µm in one frame, with an axial precision of about 30 nm (Fig. 2). With a 4× objective the same method measured a step of about 200 µm across a field more than a millimeter wide (Fig. 3).
Fig. 2. A tilted silicon wafer under a 100×, 1.45 NA objective. A: the sample. B: the mask and the calibrated spot shapes at six heights. C: the projected spots through a standard aperture and through the mask. D: the measured surface; color shows the deviation from a fitted plane, within ±0.1 µm. From Gordon-Soffer et al. (2020).1Fig. 3. A stepped sample under a 4×, 0.13 NA objective, with a mask calibrated over ±300 µm. H: the measured surface, its two levels about 200 µm apart. From Gordon-Soffer et al. (2020).1
Extended depth of field
Fig. 4 shows two samples, each imaged with a standard objective and with an extended-focus mask: dots printed on both faces of a transparent sheet 100 µm thick, and a tilted sample, where the depth of field grew by about 1.7×.
Standard
Extended focus
Standard
Extended focus
Fig. 4. The same field with a standard objective and with extended depth of field. First pair: detail of dots printed on the two faces of a 100 µm transparent sheet; with the mask, both faces are resolved in one image. Second pair: a tilted sample.
Dense scenes and single snapshots
Where points sit so close together that their shapes overlap, a neural network reads the positions directly. In single-molecule microscopy this gave a resolution of about 50 nm in all three dimensions.2
In a high-throughput microscope with the mask built into the objective, single snapshots took the place of focus stacks, and acquisition was about 10 times faster.3
What it needs
A mask is designed for one objective and one wavelength band.
It has to sit at the aperture plane of the objective, which is reached through a relay at the camera port or inside the objective.
Height measurement needs something to read: surface texture, particles or projected spots. On a mirror-like wafer the published work projected the spots.
Extended focus trades some contrast for range. Computation restores it.
Published demonstrations so far are in surface metrology and biological microscopy. Single-shot autofocus, defect detection and spectral inspection in industrial tools are what we want to build with partners.
Where it helps
The gain is throughput: fewer focus steps for the same information.
Semiconductor and wafer inspection: surface height and defect depth without focus scanning, and fast 3D screening of large areas ahead of slower high-resolution tools.
PCB and electronics inspection, where parts stand at different heights.
Precision manufacturing and quality control.
Makers of microscopes and inspection tools who want to offer depth, extended focus or single-shot autofocus on existing optics.
Working with us
We are looking for commercial partners who want to build extended depth of field, 3D defect detection, single-shot autofocus, or volumetric and spectral imaging into their inspection tools and microscopes.
The first step is a short, non-confidential call about your optics and what limits your throughput today.
R. Gordon-Soffer et al., “Microscopic scan-free surface profiling over extended axial ranges by point-spread-function engineering,” Science Advances 6, eabc0332 (2020).
E. Nehme et al., “DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning,” Nature Methods 17, 734 (2020).
N. Opatovski et al., “Depth-enhanced high-throughput microscopy by compact PSF engineering,” Nature Communications 15, 4861 (2024).
R. Orange Kedem et al., “Near index matching enables solid diffractive optical element fabrication via additive manufacturing,” Light: Science & Applications 12, 222 (2023).
Rapid Optics Fab is 123D’s fabrication service for diffractive optical elements: phase masks, diffractive and Fresnel lenses, vortex plates and other custom elements. It is built on near-index matching, a patented method developed at the Technion, which lets a commercial 3D printer do work that otherwise needs cleanroom lithography.
A phase mask made by near-index matching
Turnaround
Two to three days per element in the published process
Relief
Hundreds of micrometers deep, where a conventional element needs about one
A diffractive optical element shapes light by delaying it. A relief of height h delays the wave in proportion to h × Δn, where Δn is the difference in refractive index between the element and what surrounds it. In air Δn is about 0.5, so a full wave of delay takes about a micrometer of relief, and the surface has to be accurate to a small fraction of that.
That precision usually means photolithography or a comparable process, with its masks, cleanroom, lead time and cost. Every change of design starts the cycle again, which makes iteration slow and custom elements expensive.
Near-index matching
Near-index matching puts the relief between two transparent materials whose refractive indices are almost equal. In the published work Δn was between 0.001 and 0.007, which stretches the required relief by two to three orders of magnitude.1 A step that was a micrometer deep becomes hundreds of micrometers deep, and the tolerance on it grows in the same proportion, into the range of a commercial 3D printer (Fig. 1).
Fig. 1a. A conventional element in air. The relief is about a micrometer deep.Fig. 1b. The same element by near-index matching: a second material fills the relief, which is now hundreds of times deeper. The light leaves with the same wavefront. Not to scale.
The method was first shown with the relief immersed in an index-matched liquid,2 and then as a fully solid element.1
How an element is made
Print the moldThe inverse of the relief is 3D printed at a resolution of tens of micrometers.
CastThe first material is poured over the mold and cured.
Release and fillThe cast is released, and a second material with a nearly matched index is cured on its relief.
Finished elementThe relief is now buried inside a solid, flat element that shapes the light passing through it.
In the published process an element takes two to three days, most of it curing time.1 A newer route forms the relief in a single grayscale exposure on a consumer-grade printer, in under ten minutes.3
Fig. 2a. A casting mold for a phase plate the size of a telescope aperture.Fig. 2b. Close-up of a printed relief. The steps are large enough to see.
What we make
Phase masks for depth-encoding and extended-focus imaging, including the masks used in 123D’s own projects.
Diffractive and Fresnel lenses and microlens arrays. A published 3 × 3 array of 2 mm lenses focused to a spot of 340 µm, against a diffraction limit of about 320 µm.1
Vortex (spiral) phase plates, including high orders. One was used in MINSTED nanoscopy, where the dark center of the beam stayed below 1% of the brightness of its ring.1
Multicolor masks that encode depth and color together.1
Aberration-correcting plates.
Large elements, up to the aperture of a telescope (Fig. 2a).
Vortex plate
Microlens array
Fig. 3. Two more elements made by near-index matching.
What to know
An element is designed for a wavelength band, and the two materials are chosen for closely matching dispersion.
The published material pairs are silicone-based and may not suit high-power lasers.1
Operating temperature is part of the specification, because the design rests on a small index difference between two materials.
The process is for prototypes, custom parts and small series. It is not a mass-production process today.
Ask for an element
Rapid Optics Fab is for optical designers and instrument makers who need a custom element quickly, who want to try several designs before committing to lithography, or who need an element too large for it.
Tell us the wavelength, the aperture and the phase profile or function you need. We will tell you whether we can make it, and how soon.
R. Orange Kedem et al., “Near index matching enables solid diffractive optical element fabrication via additive manufacturing,” Light: Science & Applications 12, 222 (2023).
R. Orange-Kedem et al., “3D printable diffractive optical elements by liquid immersion,” Nature Communications 12, 3067 (2021).
L. Leites et al., “Fabrication of customized diffractive optics in under 10 minutes via single-shot grayscale projection on a consumer-grade DLP system,” ACS Photonics (2026).
