WO2019099415A1

WO2019099415A1 - Systems and methods for profiling material layers on a substrate

Info

Publication number: WO2019099415A1
Application number: PCT/US2018/060859
Authority: WO
Inventors: Kin-Chung CHAN; Doris Pik-Yiu Chun
Original assignee: Kateeva, Inc.
Priority date: 2017-11-14
Filing date: 2018-11-13
Publication date: 2019-05-23

Abstract

A method of profiling a material layer on a substrate, the method comprising detecting reflected light from a plurality of pixels on a substrate, each pixel of the plurality of pixels containing a layer of material; calculating a thickness of the layer of material of each of the plurality of pixels based on the detected reflected light, and outputting thickness profiles for the plurality of pixels in a spatially resolved arrangement relative to a plane of the substrate.

Description

SYSTEMS AND METHODS FOR PROFILING

MATERIAL LAYERS ON A SUBSTRATE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 62/586,054, filed November 14, 2017, the entire contents of which are incorporated by reference herein.

TECHNICAL FIELD

[0002] Aspects of the present disclosure generally relate to substrates, such as used in manufacturing electronic displays, having thin film layers deposited thereon, and to methods and systems for analyzing such substrates and thin film layers thereon. More specifically, aspects of the present disclosure relate to profiling thicknesses and spatial location data of thin film layers applied in pixels of an electronic display or other arrangements on a substrate, for example for quality control and manufacturing process development and control, among other things.

INTRODUCTION

[0003] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described in any way.

[0004] Profiling thin materials, such as thin films on a substrate, has application in a variety of settings to achieve a desired result, such as manufacturing a product to meet desired specifications. As technologies progress, the margin for error in complex manufacturing has narrowed, putting additional strain on quality control mechanisms. Moreover, globalization has created increased manufacturing competition, and thus a premium has been placed on manufacturers that are able to meet demanding constraints. Examples of industries where profiling thin material layers on a substrate has become important includes the manufacturing and/or assembly of electronic displays, semiconductor production, solar cell and panel manufacturing, and other industries.

[0005] In one exemplary application, the material and/or substrate profiled can be part of an electronic display. One type of electronic display relies on organic light emitting diode (OLED) technology. OLED technology utilizes an organic light-emissive layer sandwiched between two electrodes disposed on a substrate. A voltage can be applied across the electrodes causing charge carriers to be excited and injected into the organic light-emissive layer. Light emission can occur through photoemission as the charge carriers relax back to normal energy states. The displays using OLED

technology can be much thinner than in other display technologies due to the light generating nature of the organic material eliminating the need for light sources within the display itself. OLED displays also can be fabricated to be flexible and bendable due to the compliant nature of the active OLED layers.

[0006] Manufacturing of such displays requires precision and quality control in order to produce a viable result, such as a display having sufficient quality and longevity. An electronic display panel can comprise an arrangement, generally an array, of spaced regions to receive the thin material layers that ultimately form pixels or subpixels of the display. A variety of material deposition processes can be employed in the manufacture of electronic displays, including OLED displays. One such process is via ink jet printing. In some cases, the regions for ink deposition on the substrate are demarcated by surrounding bank structures on the substrate, and thus the regions are sometimes referred to as“wells.” The properties of a display panel prior to ink layer deposition, such as bank opening size, bank wall slope, bank depth, bank pitch, and taper distance, can introduce variance in the thickness of ink deposited on the panel. Other factors that can influence deposition precision in OLED display manufacturing techniques include display resolution, fluid properties (e.g., surface tension, viscosity, boiling point) associated with deposited OLED layer materials (e.g., active OLED materials, sometimes referred to as inks), which are comprised of a combination of OLED layer material and one or more carrier fluids, and deposition techniques. In addition, after ink deposition, factors such as ink layer thickness, area aperture ratios, layer uniformity, and other relevant characteristics can significantly impact display performance.

[0007] Other applications that use thin layer(s) of material deposited on a substrate similarly can benefit from precision and accuracy, including thickness tolerances and uniformity of a deposited layer, to function and/or produce a viable product or desired effect.

[0008] Some techniques for profiling such thin layer(s) material have consequences that may not be suitable for manufacturing of certain products, such as OLED displays. For example, profiling deposited ink in an electronic display using metalizing of the substrate being tested can render the display unusable, and also requires steps that add additional time to the manufacturing process. In addition, profiling techniques that rely on spot measurements may be ineffective in measuring material that is not entirely uniform (such as a deposited ink with a slight curvature) and also may be ineffective in profiling in multiple dimensions to obtain spatially resolved information about one or more material layers deposited on the substrate (i.e., Cartesian X-Y-Z information, where X and Y are orthogonal directions along the plane of the substrate and Z is the thickness direction normal to the plane of the substrate). Moreover, spot measurement techniques may add processing time that is not feasible for large-scale manufacturing of displays where it is desirable to have profile information of significant subset of all of the pixels and/or subpixels of the display. For example, it is plausible that over a million or more material layers may need to be analyzed in a day to provide desired spatially- resolved material layer thickness information in large-scale manufacturing of displays based on the number of pixels and subpixels in a display and the number of displays that may be manufactured in a day.

[0009] There exists a need to improve upon systems and methods for profiling thin material layers on a substrate that can provide a quality control mechanism (e.g., for manufacturing electronic displays or otherwise for providing an accurate profile to achieve a desired final product) with enhanced accuracy, without undue processing time, and without undesirable effects on the product associated with other profiling techniques.

SUMMARY

[0010] The present disclosure may solve one or more of the above-mentioned problems and/or achieve one or more of the above-mentioned desirable features. Other features and/or advantages may become apparent from the description which follows.

[0011] In accordance with various exemplary embodiments the present disclosure contemplates a method of profiling a material layer on a substrate , the method comprising detecting reflected light from a plurality of pixels on a substrate, each pixel of the plurality of pixels containing a layer of material; calculating a thickness of the layer of material of each of the plurality of pixels based on the detected reflected light, and outputting thickness profiles for the plurality of pixels in a spatially resolved arrangement relative to a plane of the substrate.

[0012] In accordance with yet other exemplary embodiments of the present disclosure, a system for profiling a layer of material comprises a sensing mechanism and a computing device comprising a processor and a memory. The sensing

mechanism is positioned to detect light reflected from a plurality of pixels on a substrate, each pixel containing a layer of material. The computing device is configured to receive information corresponding to the reflected light detected by the sensor, calculate a thickness of the layer of material of each of the plurality of pixels based on the

information received, and output thickness profiles for the plurality of pixels in a spatially resolved arrangement relative to a plane of the substrate.

[0013] In accordance with various exemplary embodiments of the present disclosure, a method of profiling a layer of material on a substrate comprises directing excitation light to be incident on a layer of material deposited on a substrate, detecting reflected light from the layer of material that occurs in preselected wavelengths, wherein the wavelengths are preselected based on one or more spectral shifts observed for simulated reflected light intensity data corresponding to the material of the layer of material over a target thickness range. The method further comprises determining intensity of the reflected light in the preselected wavelength ranges, and calculating a thickness of the layer of material based on the determined intensity of the detected reflected light.

[0014] In yet other exemplary embodiments of the present disclosure, a system for profiling a layer of material on a substrate comprises an excitation source, a sensing mechanism, and a computing device comprising a processor and a memory. The excitation source is arranged to direct excitation light to be incident on a layer of material deposited on a substrate. The sensing mechanism is arranged to detect reflected light of preselected wavelengths from the layer of material, wherein the preselected wavelengths are preselected based on one or more spectral shifts observed for simulated reflected light intensity data corresponding to the material of the layer of material over a target thickness range. The computing device is configured to determine intensity of the reflected light, and calculate a thickness for the layer of material based on the determined intensity of the detected reflected light.

[0015] Additional objects and advantages will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present teachings. At least some of the objects and advantages of the present disclosure may be realized and attained by means of the elements and combinations particularly pointed out in the appended claims.

[0016] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claimed invention. It should be understood that the claimed invention, in its broadest sense, could be practiced without having one or more features of these exemplary aspects and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate some exemplary embodiments of the present disclosure and, together with the description, serve to explain certain principles.

[0018] FIG. 1 is a plan view of an electronic display with a schematic representation of an exemplary pixel arrangement in accordance with the present disclosure.

[0019] FIG. 2 is a schematic depiction of an embodiment of material layers in an OLED stack in accordance with the present disclosure.

[0020] FIG. 3 is a schematic depiction of an exemplary embodiment of a material layer on a generic substrate, annotated to show light reflections and refractions, in accordance with the present disclosure.

[0021] FIG. 4 is a graphical simulation of a relationship between wavelength of detected light and intensity of the detected light from a refracting material comprising a predetermined refraction index in accordance with an exemplary embodiment of the present disclosure.

[0022] FIG. 5A is a graphical simulation of a relationship between detected intensity for red and blue light from a refracting material comprising a predetermined refraction index and the thickness for the material layer in accordance with an exemplary embodiment of the present disclosure.

[0023] FIG. 5B is a graphical simulation of a relationship between a detected intensity ratio for red and blue light from a refracting material comprising a

predetermined refraction index and the thickness for the material layer in accordance with an exemplary embodiment of the present disclosure; the polynomial fit to calculate thickness, y, based on the ratio of blue and red reflected light, x, for the curve shown is included in the figure.

[0024] FIG. 6 is a graphical representations of a relationship between wavelength of detected reflected light for a plurality of color bands and change of material layer thickness for the detected reflected light from a film stack including an HIL layer and a conductive ITO (indium tin oxide) layer on a substrate;

[0025] FIG. 7 is a graphical representations of a relationship between wavelength of detected reflected light for a plurality of color bands and change of material layer thickness for the detected reflected light from a film stack including an FITL layer, an HIL layer, and a conductive ITO (indium tin oxide) layer on a substrate;

[0026] FIG. 8 is a graphical representations of a relationship between wavelength of detected reflected light for a plurality of color bands and change of material layer thickness for the detected reflected light from a film stack including an EML layer, and FITL layer, an HIL layer, and a conductive ITO (indium tin oxide) layer on a substrate;

[0027] FIG. 9 depicts an exemplary workflow for selecting a plurality of wavelength ranges for a light emission to be used to determine a thickness for a material layer in accordance with the present disclosure.

[0028] FIG. 10 depicts an exemplary workflow for profiling a material layer on a substrate in accordance with the present disclosure.

[0029] FIG. 1 1 depicts an exemplary representation of a color map of reflected color for varying thicknesses of an FITL layer overlying an HIL layer according to an exemplary embodiment. [0030] FIG. 12 illustrates a modified color map of FIG. 1 1 after assuming a thickness of the H IL layer.

[0031] FIG. 13 illustrates a final color map correlating color to thickness of the FITL layer of FIGs. 1 1 and 12 after constraining the LCh color space to a single revolution from +p to -p about a selected target thickness for the FITL layer.

[0032] FIG. 14 is an exemplary workflow of a profiling technique using detected color of a material layer according to another exemplary embodiment of the present disclosure.

[0033] FIG. 15 is an exemplary flow diagram illustrating an implementation of the profiling techniques to output spatially resolved thickness profiles for a plurality of pixels on a substrate.

[0034] FIG. 16 is an exemplary workflow illustrating an implementation of a color space profiling techniques to output spatially resolved thickness profiles for a plurality of pixels on a substrate.

[0035] FIG. 17 is an illustration of an output of thickness profile graphs at a cross- section of a plurality of pixels in accordance with an embodiment of the present disclosure.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0036] Reference will now be made in detail to various exemplary embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

[0037] For the purposes of this specification and appended claims, unless otherwise indicated, all numbers expressing quantities, percentages, or proportions, and other numerical values used in the specification and claims, are to be understood as being modified in all instances by the term“about,” to the extent they are not already so modified. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0038] It is noted that, as used in this specification and the appended claims, the singular forms“a,”“an,” and“the,” and any singular use of any word, include plural referents unless expressly and unequivocally limited to one referent. As used herein, the term“include” and its grammatical variants are intended to be non-limiting, such that recitation of items in a list is not to the exclusion of other like items that can be substituted or added to the listed items.

[0039] Further, this description’s terminology is not intended to limit the

invention. For example, spatially relative terms— such as“beneath”,“below”,“lower”, “top”,“bottom”,“above”,“upper”, "horizontal”,“vertical”, and the like— may be used to describe one element’s or feature’s relationship to another element or feature as illustrated in the figures. These spatially relative terms are intended to encompass differing positions (i.e., locations) and orientations (i.e., rotational placements) of a device in use or operation in addition to the position and orientation shown in the figures. For example, if a device in the figures is turned over, elements described as “below” or“beneath” other elements or features would then be“above” or“over” the other elements or features. Thus, the exemplary term“below” can encompass both positions and orientations of above and below depending on the overall orientation of the device. A device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

[0040] As used herein,“pixel” is intended to mean the smallest functionally complete and repeating unit of a light emitting pixel array. The term“sub-pixel” is intended to mean a portion of a pixel that makes up a discrete light emitting part of the pixel, but not necessarily all of the light emitting parts. For example, in a full color display, a pixel can include three primary color sub-pixels such as red, green, and blue. In a monochrome display, the terms sub-pixel and pixel are equivalent, and may be used interchangeably.

[0041] As used herein, a layer or structure being“on” a surface includes both the case where the layer is directly adjacent to and in direct contact with the surface over which it is formed and the case where there are intervening layers or structures between the layer or structure being formed over the surface.

[0042] The present disclosure can be used in applications wherein the substrate on which the one or more layers of material are disposed is a sacrificial substrate that will not become part of the final product, or alternatively is to be used as part of the final product. [0043] The profiling techniques described herein may be implemented to profile any suitable thin layer of refractive material on a substrate. In some embodiments, the profiling techniques are used to profile an electronic display, such as a substrate having OLED material layers deposited thereon. For example, the layers being profiled for an OLED substrate can be any of the hole conducting layers ( e.g ., hole injection layer and/or hole transport layer), the light-emissive layers, and/or the electron conducing layers {e.g., electron transport layer and/or electron injection layer). Moreover, the profiling techniques described herein can be implemented on dried material layers. Although not limited, the profiling techniques described herein can be advantageous when applied to profiling layers of material deposited in an inkjet printing deposition process at least because of the improvement in data acquisition and analysis speed that can be attained. Profiling techniques in accordance with various embodiments of the present disclosure are suitable for a layer having a thickness ranging from a few nanometers to several thousand nanometers, for example, for OLED displays, the thickness ranges from 50 nm to 500 nanometers.

[0044] Profiling thin material layers, such as material deposited in a layer having a thickness ranging from a few nanometers to several thousand nanometers (nm), using conventional techniques may have undesirable consequences, such as those discussed above. Moreover, profiling by using spot measurements may not perform X-Y-Z profiling at sufficient accuracy and speed for complex and precision manufacturing, for instance of electronic displays. In addition, profiling by using spot measurements can be susceptible to transferred vibration (e.g., vibration transferred from the surroundings to the substrate being profiled). Thus, spot measurements may require various environmental control (e.g., isolation), leading to higher costs and manufacturing system complexity, or may be otherwise ineffective for profiling at a desired accuracy.

[0045] Exemplary embodiments of systems and methods for profiling one or more thin layers of material on a substrate that are discussed herein can provide a quality control mechanism for process development, process control, error debugging, and/or quality assurance for the deposition of thin layers of material on a substrate, such as, for manufacturing electronic displays to provide an accurate profile to achieve a desired construction. The techniques can provide such quality control with enhanced accuracy and without undesirable effects associated with other profiling techniques. For example, various exemplary embodiments of profiling techniques disclosed herein may enable fast data acquisition and analysis, whereby profile data can be determined for a measurement area with a size range of less than 1 square millimeter to more than about 10 square meters. Thus, profiling techniques in accordance with various exemplary embodiments of the present disclosure provide fast acquisition of data of spatially resolved thickness measurements (i.e., X-Y-Z dimensional measurement). In this way, quality control for defect inspection and/or for X-Y-Z dimensional measurement.

[0046] In some embodiments, the X and Y accuracy of the profiling is +/- 1 pm and the Z (height from substrate) accuracy is < +/- 5 nm (X-Y-Z referring to the Cartesian coordinate system, as depicted in FIG. 3 for example, where X- and Y-directions are orthogonal and in a plane of or parallel to the substrate and Z is in the direction normal to the plane of the substrate ). Such accuracy, particularly Z accuracy, can also provide accurate uniformity results, which may significantly affect performance of certain manufactured products, such as electronic displays. In addition, the described reflection-based profiling technique may be tolerant to vibration, thus reducing the need for environmental controls (such as isolation). Moreover, various profiling techniques in accordance with the present disclosure do not require measurements to be focused onto a sub-pixel of a substrate, such as in spot measurement techniques, and thus relative long focal length optics can be used. For example, optics having a depth of field of several millimeters can be used and/or optics that allow imaging (sensing reflected light from) of a region of a substrate comprising a plurality of pixels so as to obtain reflectance data from the plurality of pixels at the same time.

[0047] Moreover, profiling techniques in accordance with one or more embodiments described herein can use sensing mechanisms comprising imaging and other optical sensors and components that are relatively inexpensive and, in some cases,“off-the- shelf” components configured for non-specific use and general image capture

applications. The profiling techniques in accordance with various embodiments also can work at a range of magnification and scale and may have large tolerances to lighting conditions.

[0048] To illustrate various principles associated with applying the profiling

techniques of the present disclosure to thin material layers of a substrate for an OLED display, reference is now made to FIGS. 1 -3. FIG. 1 is a schematic plan view of an exemplary OLED device substrate 10, with a detailed view 20 showing circuitry for six pixels formed on the surface of substrate 10. A single pixel is designated by numeral 30, and is shown to have separate red, green and blue light generating elements (32, 34 and 36). Additional circuitry (such as depicted by numeral 38) can be formed on the OLED device display substrate to assist with control over generation of light by a respective pixel well. Those having ordinary skill in the art appreciate that the arrangement of the red, green, and blue subpixels and the pixels, as well as the driving circuitry, is exemplary and nonlimiting, and various other arrangements can be used without departing from the scope of the present disclosure.

[0049] During the manufacture of an OLED flat panel display, the OLED

pixels/subpixels are formed of at least one OLED film stack, which can emit light when a voltage is applied. FIG. 2 depicts an embodiment of an OLED stack film structure on a substrate 2 that includes between an anode 4 and a cathode 14, a hole injection layer (HIL) 6, a hole transport layer (HTL) 8, a light-emissive layer (EML) 10, and an electron transport layer (ETL) combined with an electron injection layer (EIL) 12. When voltage V is applied across the anode 4 and cathode 14, light of a specific wavelength is emitted from the EML layer 10, as schematically indicated by the dashed projection E in FIG.

1 B. In various embodiments of systems, devices and methods of the present teachings, the HIL, HTL, EML, and ETL/EIL layers depicted in FIG. 2 can be deposited on the substrate by inkjet printing; however, other deposition methods such as, coating, vacuum thermal evaporation, sputtering (or other physical vapor deposition method), chemical vapor deposition, and the like, can also be used. In an inkjet printing deposition, each of the HIL, HTL, and ETL/EIL OLED stack layers has an ink

formulation including materials that define the function of those OLED stack film layers. As will be discussed in more detail below, in an embodiment, as described with reference to FIG. 1 , a pixel can include three color-generating elements (subpixels), where each element has an EML layer that emits a different wavelength of light, for example, but not limited by, red, green and blue. For various embodiments of an OLED pixel of the present teachings, each EML layer has an ink formulation including an OLED material that can emit in the targeted electromagnetic wavelength range. In other embodiments, a monochromatic display having a single subpixel for each pixel may be implemented, as well as any number or combination of color components and

associated pixels.

[0050] FIG. 3 is a schematic depiction of an embodiment of a thin layer of material on a generic substrate in accordance with the present disclosure. Substrate 100 may comprise any suitable substrate on which a thin material can be deposited and dried to form a thin film layer, such as a printed circuitry, an underlayer for semiconductor production, or any other suitable substrate. Thin material layer 102 may comprise any suitable thin material, such as dried ink used for OLED display manufacturing, or any other suitable material. Thin material layer 102 comprises a substantially uniform and predetermined refraction index such that light of one or more wavelength ranges from an emission source (schematically depicted as source 3001 ) that is incident on the layer 102 experiences a phase shift when passing through the material. Various exemplary embodiments of profiling techniques described herein rely on detecting such phase shifts to determine a thickness for thin material layer 102 based on the known

(predetermined) refraction index of the material of the layer 102. In some embodiments, intensity of light reflected from interfaces of the layer 102 (such as represented by h for the interface between the top surface of the layer and surrounding environment (e.g., air and I2 for the interface between the layer and the material underneath the layer) can be measured by a sensor 3002 and used to calculate a thickness of the layer 102. Various exemplary embodiments perform intensity measurements tuned by using various wavelengths or wavelength ranges for the emission source and/or the sensor. In other exemplary embodiments, color corresponding to the reflected intensities can be sensed and used to calculate the thickness of the layer 102.

[0051] In various exemplary embodiments, systems and methods for profiling thin materials on a substrate rely on the refraction characteristics of a thin layer of material to measure a thickness of the material layer. For example, with reference again to FIG. 3, thin material layer 102 may comprise a substantially uniform and known refractive index n. When a light source ( e.g ., 3001 ) emits light to be incident on thin material layer 102, given a normal incidence of light, two reflections may result -- one from the surface of thin material layer 102 (labeled h in FIG. 3) and one that passes through thin material layer 102 and is reflected at the interface of the thin material layer 102 and substrate 100 (labeled I2 in FIG. 3), as illustrated. The optical path difference (OPD) between these reflections can be defined by equation (1 ):

OPD = 2n(K)t (1 )

[0052] In equation (1 ), n represents the known refractive index for thin material 102 and is a function of l (the wavelength for the light incident on the layer 102, e.g., light emitted from the source 3001 ), and t represents the thickness of thin material 102.

Based on equation (1 ), the phase difference for the two reflections (d(l)) can be defined by equation (2):

d(l) = 2TTOPD / l = 4ph(K)ΐ/ K (2)

[0053] Based on equation (1 ) and equation (2), the intensity of the sum of the 2 reflected waves (/1 and k) can be defined by equation (3):

[0054] In an exemplary embodiment, intensity is a calculated metric that is

normalized over a predetermined value range (e.g., 0-100). That is, intensity is the measured reflectivity, represented as either a fraction (0-1 ) or as a percentage (0- 100%). 0 means no reflection and 1 (or 100%) means all incident light reflected. To measure the reflectivity of any sample material, before measurement is started, the incident light can be measured as a reference intensity, l(incidentjight), then the light reflected from the sample material layer can be measured, l(sample_measured), and the light reflected from a known reference sample (e.g., having known reflectivity), such as a from substrate without any coating or material layers deposited thereon, can be measured to provide R(reference_measured). The measured intensity of the material of interest, l(sample), can then be normalized to the reflectivity of the sample, R(sample), by comparing it to the measured intensity of the known reference, using the following relationship:

[0055] l(sample_measured)/l(incident light_measured)=R (sample)/R(reference). (4)

[0056] I is intensity and R is reflection in Equation (4). R(reference) can be set to 100% to normalize the calculation.

[0057] In another exemplary embodiment, it may facilitate the system setup to measure a reference sample having a known reflectivity, rather than measuring the incident light. Thus if a reference material has a known reflection of 35%, for example, the intensity of light reflected from the reference can be measured, and the sample reflection can be calculated as

[0058] R(sample) = (l(sample_measured)/l(reference_measured))^*35% (5). [0059] Using equation (3), and given a predetermined (which can be known or otherwise determined in advance) refraction index n of the material of the layer being profiled, and selected wavelength (or range of wavelengths) l, the measured reflection intensities, as provided by equation (3) above, can provide the thickness of thin material layer, such as thin material layer 102.

[0060] In one embodiment, reflection intensities can be determined for three wavelengths (such as blue, green, and red) and a thickness of a material layer, such as thin material layer 102, as defined by equation (3) given a known refraction index of the material in the layer and know wavelengths for the three color channels for which the measurements are being made. As further disclosed herein, the intensity values measured for each of the individual color channels can be used to determine a thickness or thickness range for a thin material layer.

[0061] By knowing the refraction index and a target thickness for a thin layer of material, such as the thin material layer 102, a wavelength (or range of wavelengths) can be selected that is conducive to making a thickness determination. This selection of wavelengths can be made by relying on simulation data of light intensity for different wavelengths given a target thickness and known refraction index. FIG. 4 shows a graphical simulation of a relationship between wavelength of detected light and intensity of the detected light from a refracting material having a predetermined refraction index in accordance with the present disclosure. The simulated curves maybe be obtained by application of Fresnel equations and Transfer Matrix Formalism, as those having ordinary skill in the art would be familiar with. [0062] The simulated data shown in FIG. 4 is based on a refraction index of a combined HIL, ITO, and glass substrate layer in an OLED stack wherein the ITO and substrate thicknesses are constant and the HIL has a target thickness, for example based on specifications for manufacture of an electronic display, ranging from a few nm to several hundred nm. To produce simulation data for any material layer of interest, the refraction index is predetermined, such as via experiment or otherwise provided or known in advance, for instance by a supplier of the deposited material(s). The simulated data of FIG. 4 illustrates the occurrence of spectral shifts for wavelengths across the visible range of the light spectrum for light reflected from a thin material layer using a predetermined refraction index and a varying thickness of the layer, e.g., chosen within a range of interest.

[0063] In various embodiments, based on the simulated spectral shift curves for different wavelengths, a plurality of wavelengths or wavelength ranges are selected to perform the thickness measurements of the thickness of the particular thin material layer that comprises the given refraction index and target thickness. In some exemplary embodiments, wavelengths that exhibit a spectral shift in opposite directions are selected. As illustrated in FIG. 4, the largest spectral shift in a first direction is illustrated as occurring in a first wavelength range 302 ranging from 400 nm to 420 nm

(corresponding to blue light) and the largest spectral shift in a second direction is illustrated by a second wavelength range 304 ranging from 680 nm to 700nm

(corresponding to red light). In other embodiments, individual wavelengths or wavelength ranges of other sizes may be selected. In some embodiments, the largest signal sensitivity may be determined by a software program based on the simulated data, and two or more wavelength ranges of a predetermined size (e.g., 20 nm or other selected size) with the largest spectral shifts (e.g., in opposite directions) can be selected automatically. In some embodiments, three or more wavelengths or

wavelength ranges can be selected. While, the wavelength ranges having the larges spectral shifts in FIG. 4 correspond to the visible light spectrum, the present disclosure contemplates that wavelengths or wavelength ranges in other spectrums also may be used, such as, for example, in the ultraviolet spectrum or infrared spectrum. For instance, differing wavelengths may be desired depending on factors such as the range of thickness for which measurement is desired {e.g., longer wavelengths, such as red or IR may be better suited), the sensitivity of detection for small thickness changes {e.g., shorter wavelengths, such as blue or UV may be better suited), etc. Illumination sources and detectors {e.g., cameras or other detectors) may have to be selected and paired when using differing wavelengths.

[0064] Once wavelengths or wavelength ranges are selected for measuring the thickness of a particular thin material layer, those individual wavelength ranges may be isolated to further determine simulated response characteristics based on a known light source of selected wavelengths and sensor response. FIG. 5A is a graphical simulation of a relationship between intensity for red and blue light from the simulated data of FIG. 4 for the material layer having a known refraction index and target thickness for the material.

[0065] With reference to a graph of FIG. 5A, simulated intensity values are illustrated for reflected light of wavelength ranges reflected from a thin material layer {i.e., for the blue wavelength range of 400 nm -420 nm and the red wave length range of 680 nm - 700 nm) as a function of thickness. Similar to the data of FIG. 4, the simulated data from FIG. 5A is generated based on the known refraction index for the thin film.

[0066] Once the intensity data for the selected wavelengths or wavelength ranges is mapped to thickness, one exemplary embodiment uses the ratios of the intensities of the selected wavelength ranges to generate a polynomial equation, from a fit of the data, that correlates intensity ratio to thickness. FIG. 5B is a graph showing the simulated relationship of how thickness varies based on a detected intensity ratio for red and blue light from a refracting material using a predetermined refraction index in accordance with the present disclosure. More specifically, FIG. 5B is a graphical representation of the ratio of the simulated intensity data for the red and blue color channels (wavelength ranges) illustrated in FIG. 5A. Based on the simulated curve of intensity ratio values versus thickness, a polynomial may be mapped to the data using a regression algorithm, such as for example, various commercially available data analysis programs capable of performing regression analysis and known to those having ordinary skill in the art. This polynomial equation can then be used to convert actual reflection intensity measurements, which in various embodiments may be made during the manufacturing process, of a thin material layer of interest to thickness of the thin material layer.

[0067] To generate the polynomial equation for reflectivity intensity to thickness conversion, the ratio of two wavelength intensities may be used as described above. Flowever, in some cases, one wavelength intensity, or two or more wavelengths separately to cover different thickness ranges may be used. Alternatively, a

combination of two or more wavelengths in a different way, such as, for example, the difference or the sum, for necessary sensitivity and to cover a wide thickness ranges may be used. Those having ordinary skill in the art would understand how to modify the different intensity values used for analysis using different combinations of wavelengths. Furthermore, more than one polynomial fit may be used, for example, for different thickness ranges of the material.

[0068] The generated polynomial equation can then be used to map detected (measured) intensity values over the selected wavelengths to the thickness for the particular material (e.g., HIL layer in FIG. 5B). In the illustrated embodiment, the polynomial y = 4E-07x⁶ + 7E-05x⁵-0.0038x⁴+0.1097x³ + 1 5986x² + 12.107x + 81.51 is fit to the simulated data of FIG. 5B, where x is the ratio of intensities of the blue to red reflected light and y is the thickness in nm.

[0069] Using a generated polynomial fit, given the known refraction index and target thickness, detected intensity values for the selected wavelengths (e.g., 400 nm to 420 nm (blue light) and 680 nm to-700nm (red light) in the exemplary embodiment described above with reference to FIGS. 4 and 5) can provide accurate measurements for the thickness of the thin material layer (e.g., accuracy < +/- 5 nm according to various exemplary embodiments). Those having ordinary skill in the art would appreciate that other thin material layers with a known refraction index and target layer thickness may result in selection of other wavelengths or wavelength ranges, and thus a polynomial may be generated specific to the spectral shift simulated, analyzed, and simulated for these other thin material layers.

[0070] Thus, using the profiling technique described above, a direct intensity-to- thickness conversion can be determined by using a polynomial equation fitting technique as described above. Such a technique uses minimal computing power and time, and can provide accurate thickness determinations.

[0071] As mentioned above, in some embodiments, three or more wavelength ranges may be selected, and a function of the intensity values for the three or more wavelength ranges can be used to generate a polynomial. For example, rather than a ratio of two intensity values, as illustrated in FIG. 5B, a function of the intensity ratios for three or more wavelength ranges can be graphed and a regression algorithm can be used to map a polynomial to the function. Using more wavelength intensity data can result in increased measurement repeatability, accuracy, sensitivity and thickness range. By way of example, within a thickness range, the intensities at given

wavelengths (e.g., I(wavlengthl ), I(wavelength2) and I(wavelength3)) will all either increase or decrease as the thickness increases or decreases. The sum of all three intensity signals can be used for thickness conversion for better measurement repeatability, and, accuracy. In another example, for a relatively large thickness range, one or two wavelength data may be insufficient to cover the whole thickness range. In this case, a second set of wavelength data can be used to extend the measurement thickness range.

[0072] FIGs. 6-8 are graphical representations of a relationship between reflection data and the thickness from a refracting material layer having a known refraction index in accordance with the present disclosure. . As discussed herein, given the known refraction index for the HIL layer, a target thickness, selected wavelengths or ranges of wavelengths based on the simulated spectral shift, and the resultant polynomial equation that correlates the intensity data to a target thickness, this experimental data can be used to measure the thickness of the HIL layer. The curves in FIGS. 6-8 are measured data illustrating the spectral change in detected reflection as thickness changes, and how such data can be used to selected wavelengths for measurement of an actual sample (e.g., display) of interest. The reflection percentage is defined as the percentage of the incident light reflected from the sample {e.g., material layer of interest on a substrate), which as discussed above with reference to FIG. 3, includes the light reflected from all interfaces , e.g., h and I2 in FIG. 3. It is I(l) in Equation (3) above. To determine the reflection from a layer of material of interest, the measurement technique discussed above with reference to Equations (4) and (5) can be used.

[0073] FIG. 6 depicts the relationship between the reflection and the thickness of a HIL layer of an OLED stack, with each curve shown representing data from a differing thickness of the HIL layer FIGs. 7 and 8 show similar data of the relationship between the reflection values and the thickness of a FITL layer of an OLED stack (FIG. 7) and the relationship between the reflection values and the thickness of a blue light EML layer of an OLED stack (FIG. 8). As discussed above, the predetermined refraction index may also be experimentally determined or provided for these layers. Given the known refraction index and target thickness, simulated spectra data can be used to select target wavelengths or wavelength ranges over which measurements of reflected intensity can be made. FIGS. 7 and 8 depict the wavelength ranges 702, 704 and 802, 804 selected for the HTL layer and EML layer, respectively. For each layer, given the known properties and simulated data, a polynomial may be generated that maps intensity data over the wavelength ranges to a target thickness. [0074] The data in FIGS. 6-8 are the reflectivity spectra of HIL (hole-injecting layer), HTL (hole transport layer), and EML (emissive material layer) layers of an OLED stack with wavelengths ranging from 240 nm-1600 nm, as measured by a spectroscopic reflectometer. The spectra in turn is used to select the sensitive wavelengths and determine the index of refraction to be used for conducting a thickness profile technique in accordance with various exemplary embodiments described herein.

[0075] In exemplary embodiments, the index of refraction can be determined by analyzing spectroscopic reflection spectra or spectroscopic ellipsometric spectra using spectral analysis software. Those having ordinary skill in the art would be familiar with such techniques.

[0076] FIGs. 9 and 10 depict exemplary workflows in accordance with the profiling technique described above. FIG. 9 depicts steps performed before reflectance measurements are taken from an actual layer of material for which profiling is desired, such as a dried ink layer of an OLED stack in accordance with various exemplary embodiments described herein. FIG. 10 depicts steps performed to convert detected reflectance measurements from the layer of material of interest to thickness. Although FIGs. 9 and 10 depict steps performed in a particular order or arrangement, one of ordinary skill in the art, using the disclosure provided herein, will appreciate that the various actions depicted can be omitted, rearranged, combined, and/or adapted in various ways.

[0077] Referring now to FIG. 9, an exemplary workflow for selecting a plurality of wavelength ranges for a light emission to be used in the measurement phase ( e.g ., an exemplary workflow being shown in FIG. 10) to determine a thickness for a thin material layer in accordance with an exemplary embodiment of a profiling technique of the present disclosure is depicted. The workflow of FIG. 9 will be discussed with reference to the exemplary thin material layers, such as those illustrated in FIG. 3 and FIG. 6. But, the exemplary workflow illustrated in FIG. 9 can be implemented with any suitable material layers and detection systems.

[0078] At 902, a plurality of wavelengths or wavelength ranges may be selected based on known characteristics for a thin material layer of interest for profiling. For example, referring to FIG. 3, based on known characteristics for thin material layer 102, including a refraction index and a target thickness of the actual material layer to be deposited, spectral data is simulated to determine wavelengths and wavelength ranges that are sensitive to spectral shift, as illustrated and discussed with reference to FIG. 4. As discussed above, such simulated spectra data can be generated by using a program based on Fresnel Equations, in which indices of refraction and target thicknesses are input, and reflectivity for the material calculated by Fresnel equations for single or multiple material layers. The reflectivity for the reference material also can be calculated by Fresnel equations.

[0079] For example, the simulated data shown in FIG. 4 is illustrative of spectral shift for wavelengths across the visible range of the light spectrum for light reflected from a thin material H IL layer of a known refraction index and a target thickness. In an embodiment, the simulated data may be based on the refraction index of, for example, various hole conducting or EML layers in an OLED stack and a target thickness, but other types of material layers, may also be determined in the same manner. [0080] At 904, spectral shifts in multiple directions may be identified based on the simulated data. For example, the spectral shift may be shifts in one of two directions, and the largest spectral shifts in each direction may be identified. In other

embodiments, the two largest spectral shifts in any direction may be identified. In some embodiments, the largest spectral shifts in each direction may be determined by software program based on the simulated data. For example, the shift values may be analyzed for magnitude (e.g., delta value) and direction (e.g., positive or negative delta value) such that the largest or two largest shifts in each direction can be identified.

[0081] At 906, a number of wavelengths or wavelength ranges may be determined (selected). For example, based on the identified spectral shifts, the values for these shifts, and the directions for these shifts, a number of wavelengths or wavelength ranges can be selected and used to calculate thin material layer thickness. In an embodiment, the default number of wavelengths or wavelength ranges is two, however it may be determined that additional wavelengths or wavelength ranges are to be used to calculate thin material thickness based on a criteria for the identified spectral shifts.

[0082] For example, when the magnitude (e.g., delta value) for a spectral shift in a given direction fails to meet a criteria (e.g., a threshold delta value), it may be determined that multiple wavelength ranges in that direction should be used to determine thin material layer thickness, as described above. In other embodiments, three or more wavelength ranges may be selected.

[0083] At 908, the determined number of wavelength ranges may be selected, wherein the selected wavelength ranges correspond to spectral shifts in multiple directions. For example, where a default number of wavelength ranges is determined, two wavelength ranges may be selected. In some embodiments, the wavelength ranges that correspond to the largest spectral shifts in opposite directions are selected. For example, as illustrated in FIG. 4, the largest spectral shift in a first direction is illustrated by a first wavelength range 302 between 400 nm-420 nm (blue light) and the largest spectral shift in a second direction is illustrated by a second wavelength range 304 between 680 nm-700nm (red light). In the illustrated example, wavelength ranges 400 nm-420 nm and 680 nm -700 nm may be selected.

[0084] In other exemplary embodiments, such as when it is determined that three wavelength ranges are to be selected, two wavelength ranges that correspond to the two largest spectral shifts in a first direction are selected and a third wavelength range that corresponds to the largest spectral shift in a second direction is selected. For example, when the largest identified spectral shift in a first direction does not meet a criteria (e.g., threshold delta value) it may be determined that the sensitivity for the thickness calculation would benefit from multiple wavelength ranges being selected for shifts in the first direction. The three wavelength ranges may be selected in a manner similar to that described herein for the default two wavelength ranges. The selected wavelength ranges may be used to select a light source and sensor for actual measurements during profiling. A set of simulated film thickness versus curves of reflectivity of the selected wavelength ranges as will be detected by a sensor may then be generated at 910 over thickness ranges of interest, such as the curves discussed above with reference to FIG. 5A. As noted above, in the simulated data of FIG. 5A, light source spectral distribution and sensor quantum efficiency are assume to be 1 over all wavelengths. For actual measurements, in an exemplary embodiment, the light source spectral distribution and the sensor quantum efficiency can be measured or provided by a manufacturer. The detected reflection intensity of the actual system can be determined by (simulated reflection spectra)^*light source spectral distribution)^*(camera sensor quantum efficiency). Using a ratio of the intensities detected over the thickness range, a fit to the data, for example, a polynomial equation can be determined to map thickness as a function of reflected intensity ratio. The determination of the polynomial fit occurs at 912 in FIG. 9.

[0085] As discussed above, determination of the polynomial fit can occur in various ways and is not limited to choosing two wavelength ranges and a ratio of intensities of the two wavelength ranges. Other ways to determine the polynomial fit include, but are not limited to using a fit based on a sum of intensities, multiple wavelengths and intensities and corresponding multiple polynomial fits, a difference of intensities, etc.

[0086] In various exemplary embodiments, once the polynomial expression mapping thickness as a function of reflected intensity has been obtained, actual intensity measurements on the thin material layer of interest deposited on a substrate can be made and the corresponding thickness calculated. An exemplary workflow for such measurement and thickness determination phase is depicted in FIG. 10. At 1002 in the workflow of FIG. 10, a plurality of wavelengths or wavelength ranges for the incident light from a light source and for the detected light from a sensor are selected based on predetermined characteristics for the thin material layer, and as described above. At least two wavelengths or wavelength ranges can be selected based on the simulated data in an exemplary embodiment, but as discussed above, other number of

wavelengths or wavelength ranges can be selected. [0087] At 1004, excitation light from an excitation source is emitted and directed so as to be incident on one or more material layers, for example, on a substrate, including the material layer of interest being profiled. For example, referring back to FIG. 3, excitation light from source 3001 may be incident on layer 102 of substrate 100. The excitation source may be selected, or may otherwise be filtered, to produce incident light of the selected wavelengths or wavelength ranges onto the substrate.

[0088] At 1006, light reflected form the substrate may be detected at a sensor. For example, again with reference to FIG. 3, the emitted incident light (e.g., of the selected wavelengths or ranges of wavelengths) may be reflected from the surface of thin material layer 102 as well as reflected after passing through thin material layer 102 at the interface of thin material layer 102 and substrate 100. A sensor 3003, such as, but not limited to, a CMOS or CCD sensor, may detect intensity values for these light reflections, illustrated as h and in FIG. 3. Various exemplary embodiments may use a sensor capable of taking multiple images simultaneously with different wavelengths for each image, such as, for example, various commercially available high speed area scan or line scan color cameras with a CCD or CMOS sensor.

[0089] At 1008, a thickness of the material layer of interest may be determined based on the detected reflected light at the sensor and use of the polynomial fit equation, generated in the manner discussed above with reference to FIG. 9 or the other exemplary embodiments described above, for the thin material layer being measured.

[0090] As discussed above, a thickness of the thin material layer is calculated based on the detected intensity ratios or other values (e.g., sum of intensities, difference of intensities, relationship using more than two wavelengths, differing polynomials for different wavelengths) depending on the polynomial fit, for instance at a given X-Y position of the thin material layer. For example, the intensity ratio for detected light comprising the selected wavelengths or wavelength ranges can be input into the polynomial to arrive at a thickness for thin material layer 102.

[0091] As discussed herein, the thickness measurement, or Z data, may be combined with X and Y data to arrive at an X-Y-Z profile for a thin material layer, such as ink layers deposited on an OLED stack. When depositing layers of the OLED stack, the methodology illustrated in of FIG. 9 may be repeated for each layer of interest to determine profiles for the individual layers. That is, a first layer may be deposited on a substrate, and, after drying, the reflection profile techniques disclosed herein may be implemented to determine layer thickness. X and Y data also may be determined based on sensor imaging (e.g., using a CMOS or CCD sensor). Subsequently, a second layer may be deposited on the first layer, and this process may be repeated to arrive at the profile characteristics (e.g., X-Y-Z data) for each layer of the stack. The detected reflectivity of a multiple layer film structure is the total reflectivity from all boundaries of layers, with the layers having different index of refraction (otherwise all layers can be treated as a combined single layer). While the profiling technique of the present disclosure does not measure a thickness of two or more layers simultaneously, it is possible to measure multiple layer thickness one layer at a time, using the derived conversion polynomial equation as discussed above for each layer. Moreover, real-time feed-back of measured thickness of each measured layer can be provided before the next layer thickness calculation is started. [0092] In another exemplary embodiment, color may be detected from images of a thin material layer on a substrate and used to calculate the thickness of the material layer. Such a profiling technique may find particular application where the level of accuracy needed can be achieved using commercially-available, general purpose illumination sources and cameras that can be optimized with filters and/or other optics to produce color images. That is, recognizing that the thickness of a thin layer of material correlates to the color reflected from that layer, and as perceived by the human eye, one embodiment of a profiling technique in accordance with the present disclosure can image a material layer under a relatively wide range of lighting conditions and calculate thickness with sufficient accuracy based on color detected from an image sensor. For example, profiling the thin material layers of an OLED stack on a substrate to be used in an OLED display can be performed using such a color-based profiling technique because the accuracy of interest relates to the perception of the human eye to observe color of the ultimate display.

[0093] As in the profiling technique described above with respect to FIGS. 4-10, optical properties ( e.g ., refraction index) of the material layer of interest and a targeted thickness of the layer, as well as optical properties of a reference underlying layer, such as a substrate or another layer in an OLED stack for example, can be used to generate an algorithm that maps detected color to thickness. In an exemplary embodiment, CIE LCh (Commission Internationale de I’Eclairage Lightness-Chroma-Hue) color space can be used to map the detected color to thickness for an imaged material layer of interest. In the CIE LCh color space, L represents lightness, C represents chroma, and h is the hue angle. These values are represented using cylindrical coordinates, where L is the vertical axis and ranges from a value of zero (0) corresponding to absolute black (no lightness) to 100 corresponding to absolute white (maximum lightness); C is the distance radially from the L axis and ranges from zero (0) at the center corresponding to unsaturated (neutral gray, black or white) to 100 or more at the edge of the circle corresponding to full saturation (color purity); and h is the angle ranging from 0°-360°), with 0° (red), through 90° (yellow), 180° (green), 270o (blue), and back to zero or 360o.

[0094] For example, a model that maps reflected color as the thickness of the layer of interest and the thickness of an underlying layer change for target thickness ranges of interest. An example of such a red, green, blue color map of reflected color for varying thicknesses of an HTL layer over an HIL layer is depicted in FIG. 1 1 , which is shown in a gray scale for purposes of the application disclosure, but should be understood to show various colors as one of ordinary skill in the art would understand. As can be seen by the representation, because the reflected colors over the map can correspond to more than one thickness of the layer of FITL layer of interest, one or more filters can be applied to disambiguate possible solutions. For example, one possible filter to apply is to assume or otherwise use a predetermined thickness of the underlying layer, such as a substrate or in the case of the example of FIG. 1 1 , the HIL layer. FIG.

12 depicts the color map of FIG. 1 1 after assuming a thickness for the HIL layer of 86 nm, for example. As in FIG. 11 , FIG. 12 shows the red, green blue, colors in gray scale for purposes of the application disclosure

[0095] To the extent there remains ambiguity of the thickness from the resulting color map, the LCh color space can be limited to a single revolution from +p to-p . Limiting the model to a single revolution should yield no ambiguity in the thickness. To apply this constraint, a target thickness of the material layer of interest, such as HTL layer of FIGs. 1 1 and 12, can again be selected. The color domain can then be defined by selecting a color difference from the color at the target thickness of the layer from and in the LCh color space such that there is no ambiguity in thickness. In other words, each color in the constrained map correlates to only one thickness value. FIG. 13 represents the modified color map after the revolution constraint is applied to the FITL layer of FIGs. 1 1 and 12 at a selected target thickness T of 125 nm (as with FIGs. 1 1 and 12, FIG. 13 also is shown in grayscale for the purposes of the application)..

[0096] While an LCh color space is used in various exemplary embodiments described herein, the presently disclosed color-based profiling techniques are not limited to such color space. Rather, any color space can be used in which one dimension of the space is hue. In an exemplary embodiment, a color space having a polar hue plane may be desirable.

[0097] The color space profiling technique can utilize a range of commercially available cameras and optics that can be used to image and detect color at sufficient accuracy to generate the color-to-thickness maps and algorithms. For example, any color filter array (CFA) system, such as a bayer filter, can be used as a sensor system.

A coaxial white light source can be used for illumination. Moreover, such image sensing devices have the ability to detect color, in particular relative color over the material layers of interest, over a broad range of lighting conditions, such a profiling technique need not rely on the use of filters or other specified emission sources. Further, such image sensing devices can be utilized within the normal lighting conditions of the application of interest, such as within processing equipment used in the fabrication of substrates, such as substrates for OLED displays.

[0098] FIG. 14 illustrates and exemplary workflow of a profiling technique relying on color space to thickness mapping. As with FIGs. 9 and 10, although FIG. 14 shows a particular order of steps, such order is not so limited and other arrangements are contemplated, as well as the addition or modification of steps as those having ordinary skill in the art would understand based on the present disclosure.

[0099] At 1402, using optical properties of a material layer of interest for which it is desired to determine thickness, the target thickness of the layer, and optical properties and thickness information of an underlying layer (substrate or otherwise) the material layer of interest is deposited on, a color map can be generated to show color variation as a function of thickness of both the layer of interest and the underlying layer. An exemplary color map according to this step is shown in FIG. 1 1.

[0100] At 1404, one or more constraints are applied to the color map generated at 1402 to facilitate disambiguation of the color map. For example, a thickness or thickness range of the underlying layer may be preselected (assumed) and the color map limited to this thickness so as to have a more limited map of color to thickness of the layer of interest. Reference is made to FIG. 12 for an exemplary color map that is generated after constraining the underlying HIL layer to a thickness of about 86 nm.

[0101] At 1406, to further constrain and disambiguate the color map, the LCh color space can be constrained to a single revolution starting from a selected target thickness of the material layer of interest that is being profiled. Using the exemplary FITL layer of FIGs. 1 1 and 12 for example FIG. 13 shows the final color map using 125 nm as a target thickness and constraining the LCh color space to a single revolution of +p to -p about the L-axis of the cylindrical coordinate system.

[0102] Based on the final color map generated at 1406, color of a deposited material layer of interest can be sensed ( e.g ., relative color intensities over the material layer region of interest) and the thickness of the material layer calculated.

[0103] In various embodiments, the profiling techniques of the present disclosure and embodiments allow for multiple pixels or subpixels of a display having a material layer of interest deposited thereon to be profiled so as to determine the thickness of the layer of interest in multiple pixels in a manner that is spatially resolved in the X-Y directions. To achieve such spatially resolved thickness profiling of multiple pixels or subpixels, the profiling techniques can be combined with a substrate image and pixel registration system and algorithm, as those having ordinary skill in the art are familiar with. Such systems can image an entire substrate or region of the substrate containing multiple pixel/subpixels and use algorithms to align (via translation and/or rotation of the image) to predetermined arrangements of pixel/subpixel arrays in the X-Y plane. These algorithms also can use input by a user to determine pixel boundaries, orientation, and locations on the substrate and relative to one another to create a general X-Y map of pixel locations on the substrate. In one embodiment, registration can be performed through use of an encoded stage supporting the substrate, as those having ordinary skill in the art are familiar with. Once the registered image of the multiple

pixels/subpixels on the substrate is determined, the thickness profiling techniques can be used to determine thickness information for the material layers of interest in each of the pixels/subpixels across the entire area of the pixel/subpixel. The profiling techniques described herein allow for such spatially resolved material layer thickness information to be obtained with sufficient accuracy in a time period that permits real-time analysis during production. The profiling techniques herein are not confined to a particular resolution of a display (ppi). However, both accuracy and time scale with resolution (inversely with magnification). Accordingly, a tradeoff between speed and accuracy exists, and can be balanced depending on the particular purpose of application.

[0104] Thus, an image of a region of substrate containing a plurality of

pixels/subpixels can be detected by a sensor (e.g., CMOS image sensor) such that X and Y profile data value can be determined for the printed layers in those

pixels/subpixels. For example, X and Y information can be calculated from the imaging sensor pixel size, pixel number, and the imaging optics magnification, as those having ordinary skill in the art would be familiar with. For example, a CMOS or other similar sensor may achieve an image of sufficient quality such that analysis of the color values or intensities of reflected light for the image results in X and Y profile data within a particular accuracy range (e.g., +/- 1 pm). The X and Y profile data may then be combined with the thickness profile data from the described reflection/intensity techniques to determine X-Y-Z profile data for the thin material layers.

[0105] FIG. 15 depicts an exemplary flow diagram showing an implementation of the profiling techniques in accordance with the present disclosure for application to achieve spatially resolved thickness profile data (X-Y-Z profile) for multiple layers of materials deposited in an X-Y plane, such as for example ink printed in pixels/subpixels of an OLED display. The flow diagram depicts two algorithm/mapping systems integrated together. One such algorithm mapping module is depicted at 1510 and relates to the thickness mapping and algorithms discussed above and the other the pixel/subpixel array registration module 1515 discussed above. Input to the material layer thickness mapping/algorithm 1510 includes at 1506 optical properties ( e.g ., refraction index) of the material layer of interest and any underlying layer and/or substrate and at 1508 the targeted (desired) layer thickness and/or individual stack layer thicknesses. Input to the registration module includes, for example, the image or other sensed detection of the substrate or region of the substrate with multiple pixels/subpixels having material layers deposited in the pixel/subpixel arrangement. The mapping/algorithm at 1510 can be any of the thickness profiling techniques described above, for example, the polynomial fit mapping algorithm of detected reflected light intensity to thickness or the direct color to thickness mapping techniques. From module 1510, the thickness map based on detected color or detected or intensity can be output at 1512.

[0106] Output 1517 from the module 1515 can be a pixel/subpixel registered image taken from the sensor, which can be a raw red, green, blue image or other intensity- based image of detected light. The registered image data of 1517 can then be used as input 1519, along with the output 1512, to the overall spatial mapping at 1520 of the thickness across multiple pixels/subpixels in the X-Y direction. Thus at 1522, thickness calculations for registered pixels can be determined. That thickness calculations of registered pixels, combined with various threshold criteria or other measurements at 1521 can then be input for further processing and analysis at 1524, for example to determine at 1526 various quality control metrics, such as quality of the produced substrate and material layers thereon, process control for further fabrication of the already printed substrate or a new one, process development, etc.

[0107] With reference to FIG. 16, another exemplary workflow diagram showing an implementation of a color based profiling techniques in accordance with the present disclosure for application to achieve spatially resolved thickness profile data (X-Y-Z profile) for multiple layers of materials deposited in an X-Y plane, such as for example ink printed in pixels/subpixels of an OLED display. As with other workflow diagrams, the arrangement and order of the actions shown in FIG. 16 is not intended to be limiting, and other orders for certain steps, and/or addition or omission of steps can be contemplated by those having ordinary skill in the art.

[0108] At 1610, spectral reflectances may be determined, for example by using transfer matrix formalism or other similar known technique, for a relevant thickness regime, such as for HIL, FITL, EML layers of an OLED stack, and for wavelengths of visible spectrum ( e.g ., 380 nm - 780 nm). Once the spectral reflectances are

determined, at 1612, the spectral reflectances can be converted to a profile connection space then to a color space. By way of non-limiting example, the spectral reflectances can be converted to a profile connection space, such as Tristimulus XYZ and then to a radial LCh color space. The results of the color space conversion can then be constrained at 1614 to disambiguate data into 1 to 1 solution of color to thickness, and from that information, a look-up-table (LUT) can be built that maps detected color in color space to thickness. In an embodiment, the constraint to disambiguate the data can be the target thickness range of the application of interest. [0109] The workflow of FIG. 16 also involves registration of the pixels/subpixels of a substrate to obtain the X-Y spatial data of interest. At 1615, a color, e.g., red, blue, green, image of a sample substrate of interest can be captured and a scaled and spatially labeled map of the same can be applied to select only regions of interest such as sub-pixel structures. At 1617, the spatially labeled image may be sliced into smaller ROI (region of interest) images grouped by an indexing scheme such as subpixel ID to generate registered images. At 1619, the registered images can be converted to the color space of the look-up-table by color transformation of image’s RGB working space through profile connection space, similar to that described above. Once both the registration of the pixels on the substrate and the look-up-table has been performed, the look-up table can be applied to an imaged area of a substrate comprising the registered pixels/subpixels and a spatially resolved thickness profile output can be obtained at 1622.

[0110] It is further contemplated that the profiling techniques described herein can be used to provide the spatially resolved thickness information in various forms in addition to tabulated numerical data to facilitate use of the data for quality control and inspection. FIG. 17, for instance, shows a representation of a plurality of pixels P with thickness information conveyed as a heat map (again for purposes of the application the heat map is shown in gray scale but those of ordinary skill in the art would understand other color schemes can be employed). Displaying the information as a heat map can highlight trends over the pixels in thickness deviations and/or outlier thickness deviations. FIG.

17 also graphically shows thickness information in line graph form corresponding to cross-sectional slices of pixels, where a cross-section line 1700 can be respositionable by a user to selected various regions of pixels across a row or column of a display, as those of ordinary skill in the art would be familiar with. Those having ordinary skill in the art would appreciate that the thickness profile information can be provided in numerous other ways, with those of FIG 17 being non-limiting, to facilitate analysis and

understanding of the information to an end user.

[0111] The described exemplary embodiments have various practical applications, for instance in manufacturing of substrates with thin material layers deposited thereon. For example, analyzed data for deposited ink layers of an electronic display ( e.g ., OLED display) may be used to determine quality control metrics for various ink products, deposition techniques, and other manufacturing processes described in this disclosure. For example, the ink or film profile, thickness, and uniformity are related to process conditions for manufacturing. By monitoring the evolution of these parameters using a design of experiments (DOE) methodology, manufacturing processes may be better controlled and updated, whether in advance or during production. A process may be established where routine measurement is used to determine the stability of the process and improve quality. For example, drift from a target profile, thickness, and uniformity can be an indication of loss of quality. Profiling techniques in accordance with various embodiments of the present disclosure also may be used as quality assurance of substrates against final product specifications.

[0112] Exemplary embodiments that include an electronic display can be used with any size display and more particularly with displays having a high resolution. While OLED displays have been described herein, other types of electronics display may also be implemented in accordance with the present disclosure. [0113] Although various exemplary embodiments described contemplate utilizing inkjet printing techniques for electronic displays, the various pixel and sub-pixel layouts described herein and the way of producing those layouts for an OLED display can be manufactured using other manufacturing techniques such as thermal evaporation, organic vapor phase deposition, organic vapor jet printing. Although only a few exemplary embodiments have been described in detail above, those having ordinary skill in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.

[0114] It is to be understood that the various embodiments shown and described herein are to be taken as exemplary. Elements and materials, and arrangement of those elements and materials, may be substituted for those illustrated and described herein, and portions may be reversed, all as would be apparent to one skilled in the art after having the benefit of the description herein. Changes may be made in the elements described herein without departing from the spirit and scope of the present disclosure and following claims, including their equivalents.

[0115] Those having ordinary skill in the art will recognize that various modifications may be made to the configuration and methodology of the exemplary embodiments disclosed herein without departing from the scope of the present teachings.

[0116] Those having ordinary skill in the art also will appreciate that various features disclosed with respect to one exemplary embodiment herein may be used in combination with other exemplary embodiments with appropriate modifications, even if such combinations are not explicitly disclosed herein.

[0117] It will be apparent to those skilled in the art that various modifications and variations can be made to the devices, methods, and systems of the present disclosure without departing from the scope of the present disclosure and appended claims. Other embodiments of the disclosure will be apparent to those skilled in the art from

consideration of the specification and practice of the disclosure disclosed herein. It is intended that the specification and examples be considered as exemplary only.

Claims

WHAT IS CLAIMED IS:

1. A method of profiling a material layer on a substrate, the method comprising:

detecting reflected light from a plurality of pixels on a substrate, each pixel of the plurality of pixels containing a layer of material;

calculating a thickness of the layer of material of each of the plurality of pixels based on the detected reflected light; and

outputting thickness profiles for the plurality of pixels in a spatially resolved

arrangement relative to a plane of the substrate.

2. The method of claim 1 , wherein calculating the thickness comprises converting color of the detected reflected light to thickness.

3. The method of claim 2, wherein detecting the reflected light comprises taking a color image of the plurality of pixels on the substrate.

4. The method of claim 3, further comprising converting the color image of color values in LCh color space, wherein calculating the thickness of the layer of material of each of the plurality of pixels comprises converting the color values in LCh color space to thickness.

5. The method of claim 1 , wherein the calculating the thickness comprises converting intensity of the detected reflected light to thickness using a derived polynomial equation.

6. The method of claim 5, wherein the polynomial equation is derived from a fit of simulated intensity data over preselected wavelengths for a predetermined refraction index and target thickness of the layer of material.

7. The method of claim 5, wherein detecting the reflected light from the layer of material of each of the plurality of pixels comprises detecting reflected light having preselected wavelengths, wherein the preselected wavelengths are chosen based on spectral shifts observed for simulated reflection intensity data for each layer of material of the plurality of pixels.

8. The method of claim 1 , further comprising registering a location of each of the plurality of pixels on the substrate based on detecting the reflected light from the plurality of pixels on a substrate, wherein the registering is used to determine the spatially resolved arrangement of the plurality of pixels.

9. The method of claim 1 , wherein detecting the reflected light from the plurality of pixels on a substrate comprises detecting the reflected light with a CMOS or CCD sensor.

10. The method of claim 1 , wherein calculating the thickness of the layer of material of each of the plurality of pixels based on the detected reflected light comprises applying a model mapping detected light to thickness, wherein the model is generated based on an optical property of the layer of material, a target thickness of the layer of material, and data corresponding to reflected light from a reference material layer underlying the layer of material.

1 1. The method of claim 1 , wherein detecting the reflected light from the layer of material of each of the plurality of pixels comprises detecting the reflected light from a layer of material having a thickness ranging from 50 nm to 500 nm.

12. A system for profiling a layer of material on a substrate, the system comprising:

a sensing mechanism positioned to detect light reflected from a plurality of pixels on a substrate, each pixel of the plurality of pixels containing a layer of material; and

a computing device comprising a processor and memory configured to:

receive information corresponding to the reflected light detected by the sensor,

calculate a thickness of the layer of material of each of the plurality of pixels based on the information received, and output thickness profiles for the plurality of pixels in a spatially resolved arrangement relative to a plane of the substrate.

13. The system of claim 12, wherein: the information corresponding to the reflected light is color information, and the computing device is configured to calculate the thickness by converting the color information to thickness.

14. The system of claim 13, wherein the color information is color values in LCh color space.

15. The system of claim 12, wherein:

the information corresponding to the detected reflected light is intensity

information, and the computing device is configured to calculate the thickness by converting the intensity information to thickness using a derived polynomial equation.

16. The system of claim 15, wherein the polynomial equation is a fit of simulated intensity data over preselected wavelengths for a predetermined refraction index and target thickness of the layer of material.

17. The system of claim 15, wherein the sensing mechanism is configured to detect the reflected light of preselected wavelengths from the layer of material of each of the plurality of pixels, wherein the preselected wavelengths are chosen based on spectral shifts observed for simulated reflection intensity data for the layer of material of the plurality of pixels.

18. The system of claim 12, wherein the computing device is further configured to register a location of each of the plurality of pixels on the substrate based on detecting the reflected light from the plurality of pixels to provide the spatially resolved

arrangement of the plurality of pixels.

19. The system of claim 12, wherein the sensing mechanism comprises a CMOS or CCD sensor.

20. The system of claim 12, wherein the computing device is programmed with a model mapping detected light to thickness, wherein the model is based on an optical property of the layer of material, a target thickness of the material layer, and data corresponding to reflected light from a reference material layer underlying the layer of material.

21. A method of profiling a layer of material on a substrate, the method comprising: directing excitation light to be incident on a layer of material on a substrate; detecting reflected light from the layer of material that occurs in preselected

wavelengths, wherein the wavelengths are preselected based on one or more spectral shifts observed for simulated reflected light intensity data corresponding to the material of the layer of material over a target thickness range; determining intensity of the reflected light in the preselected wavelength ranges; and calculating a thickness for the layer of material based on the determined intensity of the detected reflected light.

22. The method of claim 21 , wherein the excitation light incident on the layer of material has a wavelength in a range of the preselected wavelengths.

23. The method of claim 21 , wherein the preselected wavelengths comprise at least two different ranges of wavelengths.

24. The method of claim 23, wherein the simulated reflected light intensity data is based on one or more predetermined optical properties of the layer of material and a predetermined target thickness for the layer of material.

25. The method of claim 24, further comprising generating a polynomial fit using the simulated reflected light intensity data, the generated polynomial fit mapping intensity for the predetermined wavelengths to thickness of the layer of material.

26. The method of claim 24, wherein the one or more predetermined optical properties includes a refraction index of the material of the layer of material.

27. The method of claim 21 , wherein further comprising normalizing the determined intensity relative to intensity of reflected light from a reference material, wherein the calculating is based on the normalized intensity.

28. The method of claim 21 , wherein the preselected wavelengths are in a spectrum of visible light, ultraviolet light, or infrared light.

29. The method of claim 21 , wherein the wavelengths are preselected by identifying at least two wavelength ranges that correspond to a largest shift in each of a first direction and a second direction.

30. The method of claim 21 , wherein detecting the reflected light from the layer of material comprises simultaneously detecting the reflected light from each of a plurality of layers of material associated with a respective plurality of pixels on the substrate.

31. A system for profiling a layer of material on a substrate, the system comprising: an excitation source arranged to direct excitation light to be incident on a layer of material on a substrate;

a sensing mechanism configured to detect reflected light of preselected

wavelengths from the layer of material, wherein the wavelengths are preselected based on one or more spectral shifts observed for simulated reflected light intensity data corresponding to a material of the layer of material over a target thickness range; and

a computing device comprising a processor and memory, the computing device configured to: determine intensity of the reflected light detected by the sensing

mechanism; and

calculate a thickness for the material layer based on the determined

intensity of the detected reflected light.

32. The system of claim 31 , wherein the computing device is programmed with a polynomial fit of the simulated reflected light intensity data, the polynomial fit mapping intensity for the predetermined wavelengths to thickness of the layer of material.

33. The system of claim 31 , wherein the excitation source is configured to produce excitation light at the preselected wavelengths.

34. The system of claim 31 , wherein the excitation light source is configured to produce excitation light having a wavelength corresponding to visible spectrum, ultraviolet spectrum, or infrared spectrum.

35. The system of claim 31 , wherein the sensing mechanism comprises a CMOS or

CCD sensor.