Patent Application
20240186104
The present invention relates to methods and associated apparatus and methods which relate to processing tools used to create imaged layers on substrates. Massively parallel implements of electron beam or chemical species beam imaging elements may be combined to form a full substrate processing system. In some embodiments the imaging systems may be used in conjunction with cleanspace fabricators. The present invention may also relate to methods and apparatus to capitalize on the advantages of cleanspace fabricators for methods or development, design, research, and manufacturing.
A known approach to advanced technology fabrication of materials such as semi-conductor substrates is to assemble a manufacturing facility as a “cleanroom.” In such cleanrooms, processing tools are arranged to provide aisle space for human operators or automation equipment. Exemplary cleanroom design is described in: “Cleanroom Design, Second Edition,” edited by W. Whyte, published by John Wiley & Sons, 1999, ISBN 0-471-94204-9, (herein after referred to as “the Whyte text” and the content of which is included for reference in its entirety).
Cleanroom design has evolved over time to include locating processing stations within clean hoods. Vertical unidirectional airflow can be directed through a raised floor, with separate cores for the tools and aisles. It is also known to have specialized mini-environments which surround only a processing tool for added space cleanliness. Another known approach includes the “ballroom” approach, wherein tools, operators and automation all reside in the same cleanroom.
Evolutionary improvements have enabled higher yields and the production of devices with smaller geometries. However, known cleanroom design has disadvantages and limitations.
For example, as the size of tools has increased and the dimensions of cleanrooms have increased, the volume of cleanspace that is controlled has concomitantly increased. As a result, the cost of building the cleanspace, and the cost of maintaining the cleanliness of such cleanspace, has increased considerably.
Tool installation in a cleanroom can be difficult. The initial “fit up” of a “fab” with tools, when the floor space is relatively empty, can be relatively straightforward. However, as tools are put in place and a fabricator begins to process substrates, it can become increasingly difficult and disruptive of job flow, to either place new tools or remove old ones. Likewise, it has been difficult to remove a sub-assembly or component that makes up a fabricator tool in order to perform maintenance or replace such a subassembly or component of the fabricator tool. It would be desirable therefore to reduce installation difficulties attendant to dense tool placement while still maintaining such density, since denser tool placement otherwise affords substantial economic advantages relating to cleanroom construction and maintenance.
There are many types of manufacturing flows and varied types of substrates that may be operated effectively in the mentioned novel cleanspace environments. It would be desirable to define standard methodology of design and use of standard componentry strategies that would be useful for manufacturing flows of various different types; especially where such flows are currently operated in non-cleanroom environments.
In many types of substrate processing environments, a common and important processing step may include “lithography” processing where images are imparted to films of sensitive material upon the substrate. In the state of the art optical lithography is used to impart images through lithography masks. In other embodiments, electron beams are used to impart images in a controllable fashion. There may be utility for creating systems where imaging systems may be formed with large parallel combinations of imaging elements that process a full substrate simultaneously. Such imaging elements that process a full substrate simultaneously may also be consistent with the desire to reduce installation difficulties for processing tools as previously mentioned.
Accordingly, the present invention provides apparatus and methods to provide parallel implementations of electron beam or chemical species beam imaging elements to form a full substrate processing system. In some embodiments these systems may be used within a cleanspace fabricator facility. Cleanspace fabrication facilities have been defined in various patent specifications by the inventive entity, and the teachings and definition of these published specification may form a basis for understanding the utility of the inventive art herein within cleanspace environments.
The present invention also provides novel methods of utilizing these designs for processing fabs which rearrange the clean room into a cleanspace and thereby allow processing tools to reside in both vertical and horizontal dimensions relative to each other and in some embodiments with their tool bodies outside of, or on the periphery of, a clean space of the fabricator. In such a design, the tool bodies can be removed and replaced with much greater ease than is the standard case. The design also anticipates the automated transfer of substrates inside a clean space from a tool port of one tool to another. The substrates can reside inside specialized carriers designed to carry one substrate at a time. Further design enhancements can entail the use of automated equipment to carry and support the tool body movement into and out of the fab environment. In this invention, numerous methods of using some or all of these innovations in designing, operating or otherwise interacting with such fabricator environments are described. The present invention can therefore include methods and apparatus for situating processing tools in a vertical dimension and control software modules for making such tools functional both within the cleanspace entity itself and also in networks of such fabricators wherein at least one of the processing tools incorporates an imaging system comprised of a multitude of imaging elements.
In some embodiments of the invention, methods are provided which utilize at least one fabricator where the cleanspace is vertically deployed. Within said fabricator there will be at least one and typically more tool chassis and toolPods. A toolPod will typically be attached to a tool chassis directly or indirectly thorough one or more other piece or pieces of equipment which attach to the toolPod. At least the one fabricator will perform a process in one of the toolPods and typically will perform a process flow which will be performed in at least one toolPod. The toolPod may have an attached or integral toolport that is useful for the transport of substrates from one tool or toolPod to another tool or toolPod. In these embodiments, a unique aspect of the embodiments is that the first toolPod may be removed from the fabricator or factory for a maintenance activity or repair and then replaced with another toolPod. The use of the tool chassis together with a toolPod may result in a replacement that takes less than a day to perform. In some cases, the replacement may take less than an hour. There may be numerous reasons for the replacement. It may be to repair the first toolPod or it may be replace the toolPod with another toolPod where the tool within is of a different or newer design type. These methods may be additionally useful to produce a product when the substrate produced by the process flow may next be processed with additional steps including those which dice or cut or segment the substrate into subsections which may be called chips. The chips may then be assembled into packages to form a product. The assembly and packaging steps may also comprise a process flow and may include sophisticated techniques including three dimensional assembly, through silicon vias, substrate stacking to mention a few; and these steps may be performed in a cleanspace fabricator or alternatively in a cleanroom type fabricator. The assembly and packaging operations may include steps for thinning substrates as well as steps to form conductive connections between conductive contacts or contacts and other conductive surfaces. Alternatively, the end product of the assembly and packaging operations which may be an assembled product may be used in method where a conductive connection is formed between a conductive contact of the package and another conductive surface of another component or entity. Any of the major types of processing may utilize an imaging apparatus comprised of a multitude of imaging elements.
In some embodiments, the methods of producing products in the mentioned cleanspace fabricator may be useful to produce small amounts of a product. An imaging system comprised of a multitude of individual imaging elements may define a lithography option for a low volume fabricator that is both economical and cost effective in that expenses such as the production of lithography masks may not be required in some embodiments. In some cases, the fabricator environment may be useful in creating and producing imaging system components as well.
There may be combinations of toolPods and tool Chassis entities which reside in environments that resemble either cleanspace or cleanroom environments which represent novel methods based on the inventive art herein. These collections may also create methods for the use of imaging systems including a multitude of individual imaging elements. For example, a vertically deployed cleanspace may exist in an environment where there is only one vertical level in the fabricator or where there are no toolPods located in a vertical orientation where at least a portion of a toolPod lies above another in a vertical direction. Alternatively, whether in only one vertical level or in multiple levels of a cleanspace fabricator type whether vertically deployed or not there may be novel embodiments of the inventive art herein that involve collections of toolPods that are functional to produce on a portion of a process flow or even a portion of a process but utilize the methods described for fabricators.
A component of an imaging system may be formed by the combination of multiple imaging elements into a system. The combination may define an array of elements that is regular or non-regular. The imaging elements may each have the ability of emitting light, or ions or chemical species from their structures unto a neighboring substrate which may have a material or layer of material in a proximate location to the imaging elements such that it may receive the emitted light, ions or chemical species onto its surface or bulk.
An imaging system may be formed by the combination of a component of an imaging system as just discussed and a fixture to hold a substrate. There may also be alignment features on the substrate or on the fixture that may hold the substrate that may be operant to the function of the imaging system. There may also be a controller which may provide control signals to other components of the imaging system. The controller may also receive signals from other components of the imaging system. The controller may process a software algorithm or a program. In the processing by the controller, the controller may access data files stored within the controller or communicated to the controller by communication means.
There may be methods for forming an imaging system or imaging system component. An imaging system element or component may be formed individually by a process. It may then be tested in an individual fashion for metrology aspects desired for imaging processing. A subset of those elements or components that have metrology results that are within a specification range may be selected. The selected components or elements may be placed proximate to a receiving substrate and arranged in a designed pattern across the receiving substrate. In some embodiments, the receiving substrate may be round in form and have a radius approximately one inch in diameter. The receiving substrate may have had electrical connection features defined upon its surface to mate up with the components or elements that are placed thereupon. A process step to electrical connect an element to the substrate may be performed. Thereafter, the combined imaging elements may form an imaging system component that may be tested. The imaging system component may be placed proximate to a test substrate which has a layer or material that may be sensitive to electrons, ions or chemical species that may be emitted by the imaging system component. The imaging system component may next be rastered across the test surface to impart the image to the test substrate. The test substrate may be further processed such that a metrology process may be performed upon it to calibrate the imaging system component. The calibration data may be fed to a controller and the imaging system component may be included into a processing system which may be referred to as an imaging system. Thereafter, the imaging system may be used to process an image for a production substrate.
In another embodiment, the imaging elements may be formed and processed simultaneously. Techniques used for semiconductor and MEMS production may be used to create the array of imaging features on a substrate. A finished substrate with attached imaging elements may next be tested in concert with a test structure. The test structure in some embodiments may be a test wafer which may have been coated with a sensitive layer that may be sensitive to processing by the imaging elements. The resulting image structures on the test wafer may next be measure by a metrology apparatus. Comparison of the image test structure to a model may be performed to determine calibration and correction data values for the imaging system to use. Optionally a second test wafer may be processed with a repeat of the processing steps for the first test substrate but with correct values based on metrology. Next, the imaging system may be used to process an image for a production substrate.
In another embodiment, an imaging system may be formed by combining an imaging system component comprising a multiplicity of individual imaging elements. In some embodiments, the imaging system component may be formed in a round form factor common for semiconductor processing as wafers. The imaging system component may be included with other components including a wafer holding and alignment system and a controller to control the operation of the various components, collect data and run programs to construct the data into model corrections for the imaging system component. The imaging system components may be included into a processing tool that may be configured in a toolPod structure which itself may be capable of interfacing with a tool chassis structure. The toolPod comprising the imaging system may be optionally placed within a cleanspace fabrication environment. In the same environment a second toolPod may be placed and may be located at a level that may be vertically above the first tool location. A first substrate may be placed within the cleanspace fabricator. The substrate may be moved to the first toolPod with the imaging system and an imaging process may be performed upon the substrate. The substrate may next be moved to the second toolPod, and a second process may be performed by the tool in the second toolPod. In some embodiments this method may be used to process semiconductors or integrated circuit substrates, MEMS, optoelectronic, biomedical engineering substrates, or the like.
In some embodiments a method for producing an imaging system may be to use a cleanspace fabricator to produce an imaging system according to the inventive art herein. In a first step a substrate may be placed within a cleanspace fabricator. The substrate may be moved to a processing tool which in some embodiments may be located within a toolPod. Next a processing step may be performed within the processing tool. The processing step may be part of a full processing flow designed to produce the array of imaging elements that form an imaging system component. After the full processing, the imaging elements may be tested by their use upon a test substrate. After the test substrate with imaged material is further processed structure that may be measured may be formed. Next a metrology process may be performed to determine calibration data and offsets or adjustments. Thereafter the produced imaging system may be used to process a substrate to image a production pattern onto the substrate with an imaging sensitive layer.
One general aspect includes an imaging apparatus including: a first apparatus including a first substrate with a multitude of imaging elements arrayed thereupon where the imaging elements are capable of emitting an imaging signal from their structure to a material sensitive to their emissions on a surface in a vicinity of the first apparatus.
Implementations may include one or more of the following features. The imaging apparatus including the imaging apparatus and additionally including: a support component for a second substrate to be processed by the imaging apparatus; an alignment feature and alignment apparatus to measure the alignment feature; and a processor operant to collect data from imaging apparatus components, process the data and control imaging apparatus components based on the data. The imaging apparatus where the multitude of imaging elements emit electrons. The imaging apparatus where the multitude of imaging elements emit photons. The imaging apparatus where the multitude of imaging elements emit molecules. The imaging apparatus where there are more than 100, 1,000, 10,000, 100,000 or 1,000,000 imaging elements upon a substrate which may be planar. The imaging apparatus where the planar substrate is approximately round with a radius approximately one inch in dimension. The method additionally including testing the imaging system to form test structures, measuring the test structures, and calculating correction values utilizing result of the measuring. The method where the imaging system includes more than 10,000 individual imaging elements. The method where the imaging system is approximately round in form with a radius of approximately 1 inch. The method additionally including the step of: including the imaging elements into a toolPod. The method additionally including steps of placing the toolPod upon a chassis, where the chassis is part of a cleanspace fabricator; placing a second substrate into the cleanspace fabricator; placing an imaging sensitive film upon the second substrate; and performing an imaging process upon the imaging sensitive film upon the second substrate. The method where the imaging elements emit electrons. The method where the imaging elements emit photons. The method where the imaging elements emit molecules. The method where there are more than 10,000 imaging elements upon a first substrate. The method where the first substrate is approximately round with a radius approximately one inch in dimension.
One general aspect includes a method of forming an imaging system including: forming an individual imaging system element, testing the individual imaging system element, selecting the individual imaging system element based on compliance to desired specifications, forming a receiving substrate with electrical interconnect features thereon, placing selected individual imaging system elements upon the receiving substrate, and processing the placed selected individual imaging elements to electrically connect them upon the receiving substrate.
Implementations may include one or more of the following features. The method additionally including testing the imaging system to form test structures, measuring the test structures, and calculating correction values utilizing result of the measuring. The method where the imaging system includes more than 10,000 individual imaging elements. The method where the imaging system is approximately round in form with a radius of approximately 1 inch. The method additionally including the step of: including the imaging elements into a toolPod. The method additionally including steps of placing the toolPod upon a chassis, where the chassis is part of a cleanspace fabricator; placing a second substrate into the cleanspace fabricator; placing an imaging sensitive film upon the second substrate; and performing an imaging process upon the imaging sensitive film upon the second substrate. The method where the imaging elements emit electrons. The method where the imaging elements emit photons. The method where the imaging elements emit molecules. The method where there are more than 10,000 imaging elements upon a first substrate. The method where the first substrate is approximately round with a radius approximately one inch in dimension.
One general aspect includes a method for forming an imaging system including steps of: placing a second substrate within a cleanspace fabricator, moving the second substrate within the cleanspace fabricator to a processing tool within a first toolPod, processing the second substrate to form imaging elements upon the substrate, and moving the second substrate with imaging elements thereon out of the cleanspace.
Implementations may include one or more of the following features. The method where the imaging elements emit electrons. The method where the imaging elements emit photons. The method where the imaging elements emit molecules. The method where there are more than 10,000 imaging elements upon a first substrate. The method where the first substrate is approximately round with a radius approximately one inch in dimension.
One general aspect includes an imaging apparatus where the imaging apparatus includes a first substrate with a multitude of imaging elements arrayed thereupon. The imaging elements may be capable of emitting an imaging signal from their structure to a material sensitive to their emissions on a surface in a vicinity of the first apparatus. The imaging elements may be formed as field emission tips formed into silicon deposited into trenches. This deposited silicon when isolated from its surrounding materials such as dielectrics and insulators may be a filament of silicon. The emission tips may protrude from a backside of a base layer into a front-side of which the trenches are etched. That is a base layer may be etched through a front surface to form blind holes called trenches in the base layer. These trenches may be filled with various films. The base layer may be delayered from the opposite side or backside exposing the dielectric surrounded trench polysilicon filaments. In some examples, there are more than 1000 emission tips in the first apparatus. In some examples, a support component for a second substrate to be processed by the imaging apparatus is included as part of the imaging apparatus. In some examples, an alignment feature may be present on each of the imaging elements and a holder of the second substrate. The alignment apparatus may measure the alignment feature to register relative alignment. The apparatus may include a processor operant to collect data from imaging apparatus components, process the data and control imaging apparatus components based on the data.
Implementations of the imaging apparatus may also include one or more of the following features. The imaging apparatus may further include a cooling device in thermal communication with the second substrate. The imaging apparatus may further include a piezoelectric actuating device to raster the imaging apparatus. The imaging apparatus may function by rastering the imaging elements where this rastering includes at least ten steps within a distance separating two of the emission tips. In some examples the imaging apparatus may include electrical circuits that bias the field emission tips and these electrical circuits may be fabricated in a high voltage CMOS processing flow. The imaging apparatus may cause the electrical circuits to function where a bias potential that the electrical circuits applies causes an electro-potential bias of the tips to exceed 5 volts. In further examples, the bias may exceed 25 volts. In some examples, the fabrication of an imaging apparatus may start with a prefabricated embedded dram memory device which is further processed to expose an array of emission tips after further processing.
One general aspect includes a method of forming an imaging system including forming two or more individual imaging system elements. The method of forming the two or more imaging system elements may include etching a plurality of trenches into a base layer. The method may also include partially filling the trenches with conformal dielectric films. The method may also include filling the trenches with polysilicon. The method may also include finishing processing of an integrated circuit with metal layers. The method may also include processing the integrated circuit to thin a backside of the base layer, where the thinning exposes a dielectric film of one or more dielectric films which coat the polysilicon. The method may also include removing the one or more dielectric films to reveal polysilicon filaments. The method may also include etching the polysilicon filaments to form tips. The method may also include testing two or more of the individual imaging system elements. The method may also include selecting two or more of the individual imaging system elements based on compliance to desired specifications. The method may also include forming a receiving substrate with electrical interconnect features thereon. The method may also include placing two or more selected individual imaging system elements upon the receiving substrate. The method may also include electrically connecting two or more individual imaging elements to electrically connect them upon the receiving substrate.
One general aspect includes an imaging apparatus including an array of emission tips, where the emission tips include polysilicon formed into cavities in a base layer, and where the emission tips are sharpened by an etching process. The apparatus may also include a dielectric layer surrounding the emission tips at least in a portion of the emission tips that is surrounded by the base layer. The apparatus may also include electrical circuits connected to each of the emission tips, where the electrical circuits bias the tips based on data related to an image.
The accompanying drawings, that are incorporated in and constitute a part of this specification, illustrate several embodiments of the invention and, together with the description, serve to explain the principles of the invention:
In patent disclosures by the same inventive entity, the innovation of the cleanspace fabricator has been described. In place of a cleanroom, fabricators of this type may be constructed with a cleanspace that contains the wafers, typically in containers, and the automation to move the wafers and containers around between ports of tools. The cleanspace may typically be much smaller than the space a typical cleanroom may occupy and may also be envisioned as being turned on its side. In some embodiments, the processing tools may be shrunk which changes the processing environment further.
There are a number of types of cleanspace fabricators that may be possible with different orientations. For the purposes of illustration, one exemplary embodiment includes an implementation with a fab shape that is planar with tools oriented in vertical orientations. An exemplary representation of what the internal structure of these types of fabs may look like is shown in a partial cross section representation in
In the linear and vertical cleanspace fabricator of
Floor 150 may represent the fabricator floor or ground level. On the right side, portions of the fabricator support structure may be removed so that the section may be demonstrated. In between the tools and the cleanspace regions, the location of the floor 150 may represent the region where access is made to place and replace tooling. In some embodiment, as in the one in
Description of a Chassis and a toolPod or a Removable Tool Component
In other patent descriptions of this inventive entity (patent application Ser. No. 11/502,689 which is incorporated in its entirety for reference) description has been made of the nature of the toolPod innovation and the toolPod's chassis innovation. These constructs, which in some embodiments may be ideal for smaller tool form factors, allow for the easy replacement and removal of the processing tools. Fundamentally, the toolPod may represent a portion or an entirety of a processing tool's body. In cases where it may represent a portion, there may be multiple regions of a tool that individually may be removable. In either event, during a removal process the tool may be configured to allow for the disconnection of the toolPod from the fabricator environment, both for aspects of handling of product substrates and for the connection to utilities of a fabricator including gasses, chemicals, electrical interconnections, and communication interconnections to mention a few. The toolPod represents a stand-alone entity that may be shipped from location to location for repair, manufacture, or other purposes.
An imaging apparatus of various types may be used in the various cleanspace fabricator designs that have been described herein and in other referenced applications. Referring to
Referring to
There may be many different manners to form imaging elements in an array on a substrate. For example, the techniques and equipment used to process semiconductor substrates to form metal connections and interconnections such as vias may be used to form the features in the exemplary imaging elements depicted at 300. A stylus head may be formed by various chemical etching techniques, for example. Processes to form imaging apparatus may typically have degrees of error associated with their formation.
Referring to
The imaging element at item 430 may also have errors of location in different directions as depicted. In fact, all of the elements may have random amounts of error in location. In some embodiments, the errors may be sufficiently small to be within a technological need for the imaging element. In other embodiments, errors of production of an imaging array or variation of the calibration of the imaging system may be sufficiently large to require correction. In
The imaging elements may be located in the grid pattern of imaging array 410 and this pattern may have an imaging element that may have a resolution capability of a small distance. In some embodiments, the small distance may be as small as 1-10 nanometers. The spacing between imaging elements may be depicted at 470 and 480. The spacing may be such to create a regular array, or in other embodiments may be designed in a non-regular fashion. This spacing may be a few hundred nanometers in some embodiments. In other embodiments the spacing could be a millimeter or more. In some embodiments, where the spacing is an exemplary 1 micron, the imaging array must be translated numerous steps in both a vertical and a horizontal direction. To cover the entire distance, the step pattern of the entire grid array (as a whole) might be 1,000 nanometers/10 nanometers or 100 steps in each direction as an example. By combining the calibration data, the array may be controlled by a controller to write arbitrary, but defined in data forms, image patterns where the fundamental image element size may be 10 nanometers by 10 nanometers in size. As mentioned, these values are provided in an exemplary manner and the use of an imaging array with multiple elements may function similarly for numerous embodiments. When various imaging elements have errors of location as discussed, these imaging location errors may be corrected algorithmically.
In an exemplary embodiment, the imaging device may be characterized by metrology and each individual imaging element characterized individually. In an example to describe a process related to this, a state of a detector may have errors that range from −10 nanometers to +20 nanometers in a first “X” coordinate direction and −20 to +20 nanometers in a second orthogonal “Y” direction. If the step resolution on the array is an exemplary 10 nanometers between each step that moves the array, then to image the full space, the array may be stepped 1 extra time for the negative X correction, 100 times for the normal area to be imaged, and then 2 additional times for the positive X Correction. This scanning procedure may repeat each time a Y direction is stepped. The Y steps may be stepped an extra 2 times for the negative Y correction, 100 times for the normal area to be imaged and then an extra 2 additional times for the positive Y correction. Each image element may have its own correction and at the extremes of the stepping process it may be expected that only a few of the elements would be active. This process is described for exemplary purposes and each imaging system may have a different set of calibration requirements. And in some cases, the elements of an imaging array of the type described herein may have a degree of dynamic characteristic and therefore repeated calibrations may be required as the apparatus is used.
In a further exemplary vein, an array according to the descriptions herein may comprise an apparatus that has a radial dimension of an inch or approximately 25 mm. The surface of such an imaging device may therefore have approximately (3.14)*25*25 mm2 or approximately 1960 mm2. If the imaging arrays have an exemplary 10 micron distance between elements and a resolution size of 10 nanometers, then scanning may involve a default 1000 steps in each of the X and Y directions plus any required extra steps for calibration needs. As well if each imaging element covers a 100 micron square area of a surface, then each millimeter square would have 10,000 such elements therein. The entire array may comprise 19,600,000 elements. Such an array might be able to be fabricated using the tools of semiconductor manufacturing as an example. That many elements may be expected to have numerous defective elements either initially or after use. There may be a utility to creating redundancy at each element location for this purpose. Also, it may be useful to invoke processes that significantly over scan a single imaging element dimension to allow for the ability of correcting for element areas that are non-functional.
The calibration of array elements may also involve calibration of the intensity of the imaging signal of each element. In array designs where each element may have an alterable intensity of operation, the calibration result may create a set of individual intensity settings that may be applied to each element. In other designs the dwell time at each step cycle may be made to allow for the least intense element to deliver an appropriate imaging signal. In such a case, the individual elements may operate in a digital fashion toggled on and off on an individual basis to deliver a required imaging signal.
Referring to
Continuing with an emission current embodiment, the probe tip 530 may be biased by a variable voltage supply 550 which may be connected to the probe tip 530 with an interconnect 540 and to a conductive surface of an element body 570 with another electrical interconnect 560. Electrical current may flow from the probe tip 530 across a calibrated gap to a conductive substrate and through a current measuring apparatus 580. A combination of sensitive measurement devices may be used to keep a leveled distance relationship in place across a substrate of imaging elements. The element body 570 may be part of the substrate to be imaged or it may be part of a calibrated holding apparatus that holds the substrate in a calibrated fashion. In some embodiments, the thickness of the substrate may be a parameter that may need to be collected for calibration. As may be apparent, in embodiments where the distance of the imaging elements to the imaged surface is critical it may be important to keep the imaging apparatus at a stable and uniform temperature across its extents and over time periods.
Referring to
The active element 675 may be used to expose a chemically active layer 680 and a substrate 690. In some embodiments the exposure may comprise electron bombardment based on emission current emitted from the tip which may be biased through the electrical interconnect 650. In other embodiments the tip may be used to create an electric field at the surface that may be used to direct ionic species towards the chemically reactive layer. In still other embodiments, the narrowed tip may comprise a photon directing material that may be coated with a reflecting material such as a metallic film, except at the very tip. The tip may direct light from a light source such as a solid state laser or a light emitting diode to the substrate. In some additional embodiments, a tip structure may represent a nano laser device. In still further embodiments, the array may be configured with nano scaled emitters. In some cases, nano scaled emitters may be tuned to emit at wavelengths that are a fraction of the emitter dimensions or at sub-wavelength conditions. There may be numerous other types of imaging elements. Because an imaging array may have so many individual elements, the power requirements for each element may be very small. In some embodiments, the amount of time required for exposure of the entire surface may be very small.
Referring to
Referring to
An alternative type of micro imaging element may be found in reference to
Referring to
Referring to
Referring to
Referring to
Referring now to
The processor 1410 may also be in communication with a storage device 1430. The storage device 1430 may comprise a number of appropriate information storage device types, including combinations of magnetic storage devices including hard disk drives, optical storage devices, and/or semiconductor memory devices such as Flash memory devices, Random Access Memory (RAM) devices and Read Only Memory (ROM) devices.
At 1430, the storage device 1430 may store a program 1440 which may be useful for controlling the processor 1410. The processor 1410 performs instructions of the program 1440 which may affect numerous algorithmic processes and thereby operates in accordance with imaging system manufacturing equipment. The storage device 1430 can also store imaging system related data, including in a non-limiting sense imaging system calibration data and image data to be imaged with the imaging system. The data may be stored in one or more databases 1450, 1460. The databases 1450, 1460 may include specific control logic for controlling the imaging elements which may be organized in matrices, arrays, or other collections to form a portion of an imaging manufacturing system.
In the current state of the art Dynamic Random Access Memory DRAM processing and embedded DRAM process as well as other trench based processes have features that are consistent with the form of the novel imaging systems herein. In an example, a 22 nm. embedded dram process may have high voltage logic blocks in concert with dram trenches. In some examples the dram trenches may be microns deep into the silicon bulk. In some examples, the dram trenches may include capacitive films on the sidewall of the trench followed by filling them with a conductive material, such as in a non-limiting perspective polysilicon. The polysilicon may be deposited in chemical vapor deposition processes that may allow for auto-doping of the polysilicon. The processing of the electrically connected trenches according to various processes currently developed may substantially comprise initial steps to form examples of the imaging systems described herein.
There may be some additional steps as well as some modifications to the processing of semiconductor processing trenches for applications such as dram capacitors. For example, for a doped polysilicon filled trench it may be desirable to have a higher doping concertation for an imaging application since current will be flowing through the polysilicon in a fairly constant manner as opposed to transient charging and discharging of the capacitor in memory applications. The insulating capacitive films that may surround the conductive polysilicon fill may be thickened to allow for passivation capable of withstanding higher bias conditions. For example, depending on the material of the capacitive films, such as silicon oxide and silicon nitride or combinations thereof, the thickness may be increased such that a bias of up to 30 volts or more may be applied to create field emission current from a trench point, the dielectric films may be thickened to support that higher voltages.
Referring to
In some examples according to the present invention further processing is illustrated by
In some examples, after the trenches with their capacitors are isolated, the capacitor films may be removed at least to a level close to the remaining base layer 1530. Proceeding to
Proceeding to
In some examples, embedded dram processes create trenches in a 22 nm. process where the trenches may be on the order of 240 nm. in size and separation. In some examples, the resulting emission tips as discussed herein may be able to be used without rastering. In other examples, the tips may be rastered in controlled manners to perform the lithography processing.
In some examples, the multiple print head devices as have been described may be used to print single cells upon a substrate. in some examples, a droplet containing a cell in a liquid media, such as growth media, may be printed. In some other examples, the cell may be printed alone. There may be numerous types of cells that may be printed at different locations determined by a model used to control the print head. The different cells may be grown from stem cell parents obtained or created from cellular material of a patient. Through various means, the stem cells may be differentiated and grown up to larger volumes of cells for printing. The multiple print heads may be fed in channels that form a row of print heads. In other examples, each print head may be positioned with its own reservoir that may contain a sample of cells for that print head alone. The print heads may be fed by reservoirs and piping and pipetting systems, or in some examples the print head may be married to a microfluidic processing element that may allow material to be distributed to any of the means of distribution to the print heads.
In some examples, a large print head with many individual printing element, such as over 10,000 for example, may be used to print relatively large areas with cells of different types to form tissues with the deposition. In a non-limiting example, cells to be printed may be cells of an individual patient, where the printed cells are grown from a cell line that originates with the patient him/herself. Referring to
Referring to
Other organ types or tissue types may be processed in analogous means. The examples relating to kidney cells are just one of many examples which may include skin, bone, heart, liver, colon, thyroid, brain, muscle, and other types. Referring to
Exemplary Microfluidic Processing System with Chemical Imaging System
Referring to
Printing Tissue Films with Multiple Cell Types with Chemical Imaging System
Referring to
Referring to
The assembled tissue layers may be surrounded in growth media in some examples which can provide energy containing constituents, gasses, and other important nutritive compounds that the cells can ingest. The environment may support the cells and layers of cells to grow together into integrated tissues and organs. In some examples the forming of the layers before assembly may include printing of cells and regional placement of signaling molecules that can induce changes and differentiated characteristics to the cells. The changes may manifest as changes at many different levels ranging from changes in expression of proteins, in the expression of transcriptomes, in expression of epigenomes, or in changes to the genome itself. These changes may be important to the end function of the three dimensionally located cells and may impart correct function to the resulting tissues and organs that may be imaged and grown.
In some examples, the growing tissue structure may be monitored by various measurement techniques. As a non-limiting example of techniques that may be employed including the algorithmic processing related to such assessment, reference is made to the U.S. patent application Ser. No. 16/215,723 filed on Dec. 11, 2018, the contents of which are incorporated herein by reference. In some examples, in situ nondestructive measurements of physical and chemical parameters such as pH, dissolved gas concentrations, temperature and the like may be used to monitor growth. In other examples, samples of cells from the growing tissue may be obtained and studied utilizing multi omics to characterize the nature of the changes in protein expression with proteomic measurements, in transcriptomes with transcriptomic measurement, in epigenomes with epigenomic measurements and in genetics with genomic measurements. The characterizations may result in changes to the conditions of the growth media or other processing conditions to influence the cells. In some cases, the measurements may be used to One general aspect includes an imaging apparatus which may also include a first apparatus. The first apparatus may include a first substrate with a multitude of imaging elements arrayed thereupon. The imaging elements may be capable of emitting a chemical droplet from their structure to a surface in a vicinity of the first apparatus. The imaging elements may be chemical droplet emitters formed upon or attached to the first substrate, where the chemical droplet may include at least a first living cell. The apparatus may include a second surface to be processed by the imaging apparatus, and an alignment feature and alignment apparatus to measure the alignment feature. The apparatus may also include a processor operant to collect data from imaging apparatus components, process the data and control imaging apparatus components based on the data.
Implementations may include one or more of the following features. The imaging apparatus may include a second apparatus which may include a third substrate. The third substrate may include a microfluidic chemical processing element, where a channel from the microfluidic chemical processing element is in physical communication with an imaging element. The imaging apparatus may include a piezoelectric actuating device to raster the imaging apparatus. The rastering may include at least ten steps within a distance separating two of the chemical emitters. The living cell has been modified to be pluripotent. The imaging element uses electrostatics to discharge the chemical droplet. The imaging element uses gas pressure to discharge the chemical droplet. The imaging element uses ultrasonics or physical impact to discharge the chemical droplet. The imaging element uses thermal generation of bubbles to discharge the chemical droplet.
One general aspect includes a method of forming a tissue. The method may include placing a substrate within a cleanspace fabricator; processing the substrate to form at least a first region with a small form factor. The method may also include placing a microfluidic processor within the cleanspace fabricator. The method may include introducing a sample of cellular material into the microfluidic processor. The method may include separating the cellular material into at least a first and second separated collection of cells with different properties. The method may include printing cells from the first separated collection of cells upon the substrate to form the tissue, where a printing element emits droplets containing at least a first cell. The method may include maintaining a sterile environment around the substrate within the cleanspace fabricator while the cells of the tissue grow.
Implementations may include one or more of the following features. The method may include forming a tissue layer where the substrate may include collagen or collagen related materials. The substrate may include resorbable materials. Processing of the substrate may include adding at least a first molecule type to defined regions of a surface of the substrate. The processing of the substrate may include removing regions of at least a surface of the substrate.
One general aspect includes a method of forming a tissue. The method may include placing a substrate within a cleanspace fabricator; placing a microfluidic processor within the cleanspace fabricator; introducing a sample of cellular material into the microfluidic processor; separating the cellular material into at least a first and second separated collection of cells with different properties; and printing cells from the first separated collection of cells upon the substrate to form the tissue. The method may include aspects where a printing element emits droplets containing at least a first cell; The method may include maintaining a sterile environment around the substrate within the cleanspace fabricator while the cells of the tissue grow. The method may include obtaining a sample of cells from the tissue after growing. And the method may include performing a multi omic measurement upon the sample of cells, where the multi omic measurement is one or more of a proteomic, transcriptomic, epigenomic or genomic measurement.
Implementations may include one or more of the following features. The method may include examples where the multi omic measurement is performed with a microfluidic processor. Nourishment may be supplied to a growing tissue through a first tool pod which contains the substrate and receives chemical supply from a reservoir of growth media. A concentration of a chemical constituent of the growth media may be altered based upon a result of the multi omic measurement. The tissue may be removed from the first tool pod to a second tool pod where it may be placed within packaging materials. The tissue may be prepared for shipping within the first tool pod, where the first tool pod acts as a shipping container for the tissue. select or reject layers of cells or integrated layer products.
Artificial Intelligence Algorithms with Imaging Systems
In some examples, the various types of imaging systems as have been described from electron emission, photon emission, to chemical and fluid emission systems may have an electronic control system that incorporates optical and physical sensor based quality checks as well as processing result measurements such as an optical inspection scan which may in some examples also leverage state-of-the-art artificial intelligence (AI) algorithms. The algorithms may perform numerous function, but in non-limiting examples the algorithms may be used for advanced analysis and parameter adjustments to achieve precise process control.
Convolutional Neural Networks (CNNs) may be exemplary algorithms for image recognition tasks and may be well-suited for the visual inspection of quality in the performance of the various imaging processing as has been described herein. These networks may be trained on labeled datasets containing examples of both good and defective products, enabling them to learn intricate features and patterns indicative of quality standards. In some examples, the datasets may include images of physical/optical inspection scans of an imaged product. In other examples, the datasets may also include parametric data from the processing equipment such as the measurement of an emission current from electrical emission based imagers, the measurement of an operating current from a photon based processor, or the detection of an emitted droplet from a chemical based emission system.
Once integrated into a processing control system of an imaging system, the CNN may process real-time visual data, electrical data and/or sensor data obtained from the quality checks. For instance, in a production line scenario, the system can use object detection algorithms, to identify and locate specific defects within the visual input. In a non-limiting example, such defects may include regions of developed photoresist that are missing on a sample when compared to a design input, or conversely regions of developed photoresist that are present on a sample when they should not be. There may be various quality measures between these extremes such as a measurement of a line width of a feature that is narrower or wider than the designed feature aspect. The AI model may then categorize these defects and provide detailed insights into the nature of the issues, facilitating a granular understanding of the production anomalies and with matured models and processing equipment may allow for on the fly processing parameter adjustment based on statistical rules and AI modelling.
To adjust parameters for effective process control, reinforcement learning algorithms may offer a dynamic approach. Proximal Policy Optimization (PPO) or Deep Deterministic Policy Gradients (DDPG) may be examples of reinforcement learning algorithms that may be suitable for continuous control tasks. In the context of an electronically controllable system, these algorithms may optimize parameters like exposure speed, pressure, temperature, emission current, droplet charge as different examples for different imaging system types. The control adjustments may be based on the feedback received from the AI-powered visual inspection in some examples. For example, if the system identifies a recurring defect pattern, the reinforcement learning algorithm can adjust parameters to mitigate the issue in real-time.
By combining CNNs for visual inspection and reinforcement learning for parameter adjustments, the electronically controllable system may gain the ability to autonomously adapt to evolving production conditions, ensuring consistent high-quality output. This holistic integration of AI technologies exemplifies a sophisticated approach to achieving precise process control and enhancing overall manufacturing processes. An electronically controllable system with a visual check for quality output may benefit from the integration of artificial intelligence (AI) to enhance its process control. AI, particularly computer vision type algorithms, may be employed to analyze the imaging related data obtained during quality checks. This may provide the system with the capability to discern intricate details and patterns beyond the capacity of traditional rule-based methods and allow for parameter adjustment to optimize the imaging result. The capability may also allow for adjustments to the system that may occur as components drift or wear or otherwise are altered. By leveraging machine learning models trained on diverse datasets, the system can develop a nuanced understanding of what constitutes a high-quality imaging output in various scenarios.
AI-driven inspection analysis may be used to identify defects, deviations, or anomalies in the imaging result or how the product evolves or grows in some cases. In some examples, the inspection and processing may allow for real-time detection and allow for immediate corrective actions. This may occur by the monitoring of equipment parameters, for example which may have been correlated by machine learning type algorithmic process to good imaging results. This may ensure that the system adapts dynamically to changes in the production environment. Moreover, AI algorithms may learn and adapt over time, continuously improving their accuracy and reliability based on feedback from ongoing operations. This iterative learning process may contribute to a more robust and adaptive quality control mechanism.
In terms of adjusting parameters for process control, the insights gained from the AI analysis may be utilized to dynamically optimize the electronically controllable system. In some examples, if certain visual or inspection base patterns consistently precede a drop in quality, the AI system may trigger adjustments to relevant parameters, such as machine settings, production speed, or resource allocation. This closed-loop feedback mechanism may ensure that the system not only identifies issues but also proactively fine-tunes its operations to maintain or improve the quality of the output. By integrating AI into the process control framework, the electronically controllable system may gain the ability to continuously self-optimize, resulting in enhanced efficiency, reduced waste, and overall improved production quality.
By employing a GAN, the system may generate synthetic, yet realistic representations of high-quality outputs based on an existing dataset. This synthetic data may be used, in some examples, to augment the training of the AI models responsible for visual inspection and defect detection. As a result, the system may become more adept at recognizing subtle variations and potential defects in the production process, contributing to a higher accuracy in quality assessment.
Furthermore, the system may be leveraged to learn the underlying distribution of the input data, enabling the system to generate novel samples that adhere to the desired quality standards. The generated samples may serve to simulate diverse production scenarios and identifying optimal parameter configurations. The electronically controllable system may be used for its insights gained from these generative algorithms to predict how changes in parameters might affect the output quality and adjust the manufacturing process accordingly.
In essence, the integration of generative algorithms into the AI-driven framework may allow the system to not only identify and rectify defects but also to proactively explore and anticipate ways to improve output quality. In some examples, these algorithms and methods may encompass visual inspection, parameter adjustment, and generative modeling, to allow for electronically controllable system optimization for manufacturing, where it may continuously refine its processes to achieve superior and consistent results.
For some examples where an electronically controllable system may seek precise process control through AI, the implementation of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) may involves designed model architecture and iterative refinement based on process control data.
In some examples, where the CNN may be responsible for visual or other imaging base quality checks, a multi-layered architecture may be an essential aspect for well operating AI models. The initial layers of the model may typically consist of convolutional and pooling layers, extracting hierarchical features from the input visual or inspection data. Subsequent layers may be designed to capture higher-level abstractions, culminating in fully connected layers for classification tasks. During the system's operation, the CNN may undergo continuous refinement through transfer learning. In some examples, this may involves fine-tuning the pre-trained model with the ongoing process control data, enabling the network to adapt to subtle variations in product quality over time.
In parallel, the GAN may be employed for generative tasks comprising a generator and a discriminator network. The generator may synthesize realistic output samples, while the discriminator may assess their consistency to a desired output. Both networks may be refined iteratively through adversarial training. Initially, the generator produces may create samples that may not perfectly align with the desired output. However, as a GAN is exposed to additional process control data, it may refine its generative capabilities to produce increasingly realistic representations of high-quality imaging outputs. This continual refinement may ensure that the generative model may become more adept at simulating diverse production scenarios, contributing valuable insights for process optimization.
In some examples, the system may be part of a closed-loop system and the refined CNN and GAN models may work collaboratively. The CNN may utilize its improved ability to detect defects and anomalies in real-time visual or inspection data, which in turn may feed this information into the GAN for further refinement. The GAN, in turn, may generate synthetic samples based on the evolving understanding of high-quality outputs, contributing to the ongoing training of the CNN. This dynamic interaction between the two models, coupled with continuous feedback from the electronically controllable system's process control data, may establishes a self-optimizing loop that ensures the AI-driven framework may adapt to changing manufacturing conditions and consistently achieve precise process control.
In some examples, a refined set of one or more CNN and GAN models may be developed and then the underlying aspects of these models may be used to create a dedicated artificial intelligence integrated circuit or “chip” for the particular imaging system. An integrated circuit may be useful in implementing modelling at even higher processing rates and may allow for a system to operate without connection to external systems or networks. In some examples, an artificial intelligence chip may include memory and storage elements or be connected to them to allow for refinement of operating conditions of the chip, even if some of the interconnections are programmed into the hardware itself. In some examples, parts of the model on the integrated circuit may be field programable. In some examples an unaltered copy of the original set of models may exist redundantly to any adjustable iterations of such a model. Both the CNN and GAN models may be implemented in circuitry in an artificial intelligence IC and may have very high operating speeds accordingly which may also improve the quality output of the processing.
Referring to
A CNN Processing Block:2020 may perform some of the artificial intelligence algorithmic processing taking input from the input layer 2001 as described. The input may be routed through Convolutional layers 2021. These layers may extract hierarchical features from the input images or similar input data types. The output from the convolutional layers 2021 may be passed to pooling layers 2022. These layers may down sample the feature maps to reduce computational load. There may be back and forth passing of data 2025 between the convolutional layers 2021 and the pooling layers 2022 in some examples. Additionally, the pooling layers 2022 may interact 2026 with fully connected layers 2023: These layers may classify the features extracted from the input data. The CNN 2020 may communicate 2013 with an output layer 2040 that may include algorithmic processing to provide input 2015 on processing adjustments to a processing and automation control system 2060 for the imaging system. The entire leg of the artificial intelligence processing system, in some examples, may operate with a measurement feedback loop in 2019. Feedback from measurements which may be taken during the processing, as a function of the processing and automation control systems 2060 may provide real-time information on product quality. This feedback loop may be crucial for updating the CNN's weights and biases in real-time, adapting to changing conditions.
As a means of further improving the ability of the system to provide input on processing changes to the processing and automation control systems 2060, a Generative Adversarial Network (GAN) 2030 may be employed to search for improvements over current operational control. At an initial level, a model and design database 2002 may provide 2012 an initial design model as an input to the input layer 2001 of the CNN 2020. This may utilize a computational path that ultimately provides a feedback loop 2017 to the GAN 2030. In some examples, the model data may be fed 2011 as a starting point or a feedback reference to the GAN 2030. In some examples, the GAN 2030 may utilize a generator 2031 to be initialized with random noise vectors and attempt to generate synthetic samples resembling high-quality outputs which may be compared against the model and design database fed 2011 to the GAN 2030. The discriminator 2032 may be fed an output 2025 and then evaluates a quality measure of the generated samples, comparing between real and synthetic data. As the model refines and the quality of the result improves the input to the GAN for comparison may shift to the result of the CNN output layer 2017 or to actual measurement data from the processing and automation control system 2060 At early periods of the network processing, the feedback process may operate at a synthetic level where a parallel CNN 2020 to the actual equipment control scheme may be operated and feedback may be provided to optimize the operation of the GAN. In some examples, at some point the GAN may begin to provide inputs 2018 directly to the processing and automation control systems 2060, or by routing the synthetic result through the CNN Output layer 2040 path 2015.
The CNN may be continuously updated based on measurement feedback to improve defect detection and overall quality assessment. And the GAN's generator may adapts to feedback as described and generate more realistic and quality-aligned synthetic samples that may move processing setpoints to operating points outside of “local minimums” that may result in further improved results.
In some examples, an AI Chip architecture may operate with both the CNN And GAN network operations being processed at the imaging system. In some examples, a hybrid mode of operations may involve an AI Chip processing some of the Network processing stems of either the CNN or GAN while communication to other processing systems. In some other examples, the AI Chip may operate in a parallel mode with cloud based processing aspects.
In some practical examples, the result of the artificial intelligence calculations may adjust operating conditions such as the rate of operations of imaging system components, the control signals and operating currents or flow aspects and the like. Since some imaging systems may include a very large number of imaging elements, the described AI operations both with communication related processing as well as AI Chip operation directly at the imaging systems may occur in a parallel sense with a plethora of AI Chips or AI Network nodes operating individually on a subset of the imaging device structure in much the same manner as has been described.
In some examples an imaging apparatus may be assembled including a first apparatus including a first substrate with a multitude of imaging elements arrayed thereupon, wherein the imaging elements are capable of emitting an imaging signal from their structure to a material sensitive to their emissions on a second surface in a vicinity of the first apparatus. The apparatus may also include examples including this second surface to be processed by the imaging apparatus, but the second surface may be moved into and from the apparatus. In some examples, the apparatus may include an alignment feature and alignment apparatus to measure the alignment feature which may be used to register the first substrate to the second substrate. In some examples, the apparatus may include a processor, which may include means to store or directly process artificial intelligence algorithms. In some examples, the apparatus may be operant to collect data from imaging apparatus components, process the data and control imaging apparatus components based on the data, which may be processed using the artificial intelligence algorithms.
In some examples, the artificial intelligence algorithms may be implemented by loading the algorithms into the processor from a data storage component associated with the processor. In some other examples, the artificial intelligence algorithms may be implemented, at least in part, through circuitry of an artificial intelligence chip which may be included within the imaging apparatus. In some examples, the artificial intelligence algorithms, implemented in part through circuitry of an artificial intelligence chip, may include a convolutional neural network. In some examples, the artificial intelligence algorithms, implemented in part through circuitry of an artificial intelligence chip, may include a generational adversarial network.
In some examples, the imaging apparatus may include cases wherein the output of the convolutional neural network is provided to a controlling element that alters a current flowing in one or more of the imaging elements. In some examples, the imaging apparatus may include cases wherein the multitude of imaging elements emit electrons. In some examples, the imaging apparatus may include cases wherein the multitude of imaging elements emit photons. In some examples, the imaging apparatus may include cases wherein the multitude of imaging elements emit molecules.
In some examples, there may be methods of forming an imaging system which may include steps to form an individual imaging system element. The method may include testing the individual imaging system element. The method may include selecting the individual imaging system element based on compliance to desired specifications. The method may include forming a receiving substrate with electrical interconnect features thereon. The method may include placing selected individual imaging system elements upon the receiving substrate; and processing the placed selected individual imaging elements to electrically connect them upon the receiving substrate. In some examples, the methods may include testing the imaging system to form test structures and then measuring the test structures. In some examples, the method may include calculating correction values utilizing result of the measuring. In some examples, the method may include training an artificial intelligence algorithm utilizing the test structure measuring and correction value experience. In some of these examples, the method may include examples wherein the artificial intelligence algorithm processes at least in part on an artificial intelligence integrated circuit. In some of these examples, the method may include examples wherein the artificial intelligence algorithm utilizes a convolutional neural network. In some of these examples, the method may include examples wherein the convolutional neural network interacts with a generative adversarial network to simulate an imaging result for a set of operating parameters simulated for operation of the imaging system.
In some examples, the method may include cases wherein the imaging system comprises more than 10,000 individual imaging elements.
In some examples, the method may include cases comprising the step of including the imaging elements into a toolPod. In some examples, the method may include cases additionally including the steps of placing the toolPod upon a chassis, wherein the chassis is part of a cleanspace fabricator; placing a second substrate into the cleanspace fabricator; placing an imaging sensitive film upon the second substrate; and performing an imaging process upon the imaging sensitive film upon the second substrate. In some of these methods the imaging elements may emit electrons. In some of these methods the imaging elements may emit photons. In some of these methods, the imaging elements may emit molecules.
In some examples, there may be methods of forming a product. These methods may include obtaining an imaging apparatus which may severally include a first apparatus comprising a first substrate with a multitude of imaging elements arrayed thereupon, wherein the imaging elements are capable of emitting an imaging signal from their structure to a material sensitive to their emissions on a second surface in a vicinity of the first apparatus, the second surface to be processed by the imaging apparatus; an alignment feature and alignment apparatus to measure the alignment feature; and a processor, comprising an artificial intelligence integrated circuit comprising artificial intelligence algorithms, operant to collect data from imaging apparatus components, process the data and control imaging apparatus components based on the data. The method may include processing a substrate with the imaging apparatus. The method may include measuring a result of the processing of the substrate with the imaging apparatus. The method may include training the artificial intelligence algorithms with the measurement result. And the method may include processing a second substrate with the imaging apparatus as a part of processing steps to form a product.
Reference may have been made to different aspects of some preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. A Glossary of Selected Terms is included now at the end of this Detailed Description.
While the invention has been described in conjunction with specific embodiments, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, this description is intended to embrace all such alternatives, modifications and variations as fall within its spirit and scope.
This application is a continuation in part of the Utility application Ser. No. 17/688,417 filed Mar. 7, 2022, and entitled “METHOD AND APPARATUS FOR AN IMAGING SYSTEM” which in turn is a continuation in part of the Utility application Ser. No. 16/805,005 filed Feb. 28, 2020 and entitled “METHOD AND APPARATUS FOR AN IMAGING SYSTEM” which in turn is a continuation in part of the Utility application Ser. No. 16/036,663, filed Jul. 16, 2018 and entitled: “METHOD AND APPARATUS FOR AN IMAGING SYSTEM”, which in turn is a continuation in part of the Utility application Ser. No. 15/595,759, filed May 15, 2017 and entitled: “METHOD AND APPARATUS FOR AN IMAGING SYSTEM OF BIOLOGICAL MATERIAL,” which in turn is a continuation in part of the Utility application Ser. No. 15/380,649, filed Dec. 15, 2016 and entitled: “METHOD AND APPARATUS FOR AN ELECTROMAGNETIC EMISSION BASED IMAGING SYSTEM.” The application Ser. No. 14/861,737 in turn is a continuation in part of the Utility application Ser. No. 14/861,737, filed Sep. 22, 2015, and entitled: “METHOD AND APPARATUS FOR A HIGH RESOLUTION IMAGING SYSTEM.” The application Ser. No. 14/861,737 in turn is a continuation in part of the Utility application Ser. No. 14/594,335, filed Jan. 12, 2015, and entitled: “METHOD AND APPARATUS FOR AN IMAGING SYSTEM.” The application Ser. No. 14/594,335 in turn claims the benefit of the United States Provisional Application bearing the Ser. No. 61/926,471, filed Jan. 13, 2014, and entitled METHOD AND APPARATUS FOR AN IMAGING SYSTEM. The contents of each are relied upon and hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61926471 | Jan 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17688417 | Mar 2022 | US |
| Child | 18438400 | US | |
| Parent | 16805005 | Feb 2020 | US |
| Child | 17688417 | US | |
| Parent | 16036663 | Jul 2018 | US |
| Child | 16805005 | US | |
| Parent | 15595759 | May 2017 | US |
| Child | 16036663 | US | |
| Parent | 15380649 | Dec 2016 | US |
| Child | 15595759 | US | |
| Parent | 14861737 | Sep 2015 | US |
| Child | 15380649 | US | |
| Parent | 14594335 | Jan 2015 | US |
| Child | 14861737 | US |
