SYSTEM AND METHOD OF DETECTING OR PREDICTING MATERIALS IN MICROELECTRONIC DEVICES AND LASER-BASED MACHINING TECHNIQUES WITH CO2 ASSISTED PROCESSING

Systems and methods for detecting a material composition of a specimen and for cross-sectioning of the specimen. The system includes an imaging system, a femtosecond laser source, and optionally, a synchronized CO2 injection system. The imaging system is configured to capture image data of a surface of the specimen that has been etched by the laser. A machine learning model is applied to determine a predicted material composition of the specimen based at least in part on the image data. The machine learning model is trained to receive as input the image data and/or one or more quantified surface texture parameters determined from the image data and to produce as output an indication of a predicted material composition. A laser-based milling system is configured to use these material composition detection mechanisms to automatically determine when the laser system has milled through a first layer of a specimen and reached a second layer, and to adjust the operation of the milling system in response. The CO2 injection system can be used to provide fast, clean, high aspect ratio cross-sectioning of microelectronic parts for providing high-precision and high-throughput machining for material removal (e.g., for intrusive inspection of electronic components).

FIELD OF THE INVENTION

The present disclosure relates to systems and methods for determining or predicting a type of material used in small-scale samples including, for example, microelectronic devices, and to systems and methods for laser machining of microelectronics with a CO2gas delivery system for targeted access to internal structures. The present disclosure also relates to techniques for laser-based machining accompanied by the application of CO2to the surface being machined where the system is configured to synchronizing the movement of the CO2spot on the surface of the sample with the movement of the laser spot projected on the surface of the sample.

BACKGROUND OF THE INVENTION

With the ever-growing miniaturization of features in microelectronic parts, the use of destructive methods are increasingly relied-upon for inspection, failure analysis, and reverse engineering of microelectronics. Studying the buried structure of microelectronic parts with high-resolution imaging, requires material to be removed in a precise fashion to expose the region of interest. Based on the machining requirements and the composition of the material being ablated, lasering and scanning parameters must be fine-tuned in order to achieve satisfactory results. However, accurate information about the material composition of a sample of interest is often not available, and the material composition of the sample must therefore be inferred. Furthermore, endpointing of a deprocessing system becomes increasingly difficult due to the large material removal rate of laser systems.

Conventional methods for material detection, such as focused ion beam (FIB) milling, mechanical etching, and chemical etching, include a trade-off between accuracy of the material detection and throughput of the detection process. Ultrashort pulsed (USP) laser which offers a thermal material ablation, can drastically improve such trade-off, by providing fast yet precise machining. However, due to a lack of a mechanistic understanding of the laser and matter interaction, the laser machining practices are often trial-and-error, with no systematic method for generating proper machining recipes.

In some instances, non-trial-and-error-based methods that could be used for material detection include energy dispersive spectroscopy (EDS) and laser induced breakdown spectroscopy (LIBS). The complexities associated with integrating such techniques with laser machining may be a prohibitive factor on the way of using them.

Non-destructive methods such as X-ray tomography and Tera Hertz Imaging may be utilized. However, these approaches are limited in resolution of 700 nm to 1 micron. Other techniques that provide for a more thorough analysis may utilize, for example, scanning electron microscopes (SEMs), Focused Ion beams (FIBs) and Transmission Electron Microscope (TEMs). Such analyses in turn require sample preparation which entails targeted access to the areas of interest and buried layers. The stacked structure of ICs can make it difficult to access such internal attributes.

The “deprocessing” of ICs (i.e., to obtain targeted access to the internal structure of the IC) may utilize a combination of methods such as wet etching, plasma (dry) etching, grinding, and polishing. However, an advanced laboratory having one of more of the following pieces of mechanical equipment is required: a semi-automated polishing machine, a semi-automated milling machine, a laser, a gel etch, a computer numerical control (CNC) milling machine, and an ion beam milling machine. Additionally, modern IC “chips” consist of several metal layers, passivation layers, vias, contact, poly, and active layers. A reverse engineer/failure analyst would be required to determine the etchants and tolls as well as the time needed to remove each layer. Parameters may vary from layer-to-layer and from material-to-material.

When deprocessing an IC, the layer surface has to be maintained as planar, and each individual layer should be etched carefully and accurately. The planarity of the layer could be conformal or planarized. In a planarized layer, only one layer appears at a time. Conformal layers, in which some portion of the different layers and vias could appear on the same plane can make deprocessing even more complex and challenging. One must be careful to remove one material but not another (e.g., removing a metal layer without affecting the vias) during deprocessing.

In situ solutions such as focused ion beam (FIB) using conventional liquid metal ion sources may be relatively easy to use, but the milling rates are much too low for the amounts of material that need to be removed for some applications. Noble gas plasma ion source FIBs can achieve up to 20 times faster milling and can cross-section individual interconnects in much less time. However, they are still not fast enough at simultaneously cross-sectioning multiple large interconnect structures, stacked dies, or fully packaged devices. Furthermore, insulating samples suffer from poor milling due to charge accumulation. Because of these (and other) challenges, deprocessing can involve a trial-and-error process which consequently demands many samples to reach the optimized process.

Accordingly, systems and methods of detecting or predicting materials used in microelectronic devices that balance accuracy with throughput are desired. Additionally, systems and methods of conducting failure/reliability analyses of microelectronic internal structures that balance accuracy with throughput are desired.

SUMMARY OF THE INVENTION

Thus, in various implementations, the systems and methods described in the examples herein provide a technique that detects material composition based on surface texture parameters derived from confocal images of a lasered area. A multilayer fully connected neural network is trained to predict the material composition of the sample with a single image of the surface. Furthermore, although lasering may start before material composition can be detected, similar to LIBS, the amount of lasering that is needed for this purpose is minimal, for example, only using a single pass of the laser. A multilayer fully connected neural network was trained in order to predict the material composition of samples.

In some implementations, the invention provides a material detection method using femtosecond laser, confocal imaging and image processing. In some implementations, the material detection method is incorporated into a laser milling system to provide a feedback mechanism for automated end-pointing in fast inspection of microelectronics. In some implementations, the system includes an AI model that is trained to detect material composition based on surface texture parameters derived from confocal images using reflected light from the laser milled area.

In some implementations, the methods and systems described herein are configured to predict material composition during a laser machining process by utilizing surface texture extracted from confocal images of a lasered sample. After training an AI model, a 50× magnification confocal image with a field of view of 250 by 250 microns provides sufficient data in order detect the material being lasered. Furthermore, although lasering must start before material composition can be detected, the amount of lasering that is needed for this purpose is minimal only requiring a single pass of the laser. In some implementations, the AI model includes a multilayer fully connected neural network trained to receive as input one or more quantified parameters extracted from the captured image data and to produce as output a prediction of the material composition of sample(s).

In some implementations, the invention provides a technique for prediction of sample material composition using a combination of femtosecond pulse laser and confocal imaging. A neural network is trained by the surface metrics extracted from confocal images of lasered samples. The trained neural network is then able to predict the material composition, when the laser machining parameters and the confocal images were provided as input to the trained neural network. In some implementations, the method provides a fast and reliable, yet affordable solution for material characterization that is much needed for proper machining of microelectronic parts for conducting inspection, failure analysis and reverse engineering. Further, by integrating the proposed method into an automated system that will use the image processing data as a feedback to control the laser system, in real time, it can serve as an end-pointing and parameter tuning mechanism that that offers new capabilities in terms of sample preparation applications.

In one embodiment, the invention provides a material detection system including a laser system, an imaging system, and an electronic controller. The laser system is configured to controllably etch a specimen and the imaging system is configured to capture image data of the etched surface of the specimen. The electronic controller is configured to receive image data from the imaging system and to apply a machine learning model to determine a material composition of the specimen based at least in part on the image data. The machine learning model is trained to receive as input the image data and/or one or more quantified surface texture parameters determined from the image data and to produce as output an indication of a predicted material composition.

In another embodiment, the invention provides a method of training a machine learning model for a material detection system. The material detection system is operated to controllably etch a plurality of different samples and to capture image data of each etched sample. The plurality of different samples includes specimens of different material compositions. A set of training data is generated for each sample of the plurality of different samples and each set of training data includes an indication of a material composition of the sample and one or more texture parameters for the sample based on the captured image data. The machine learning model is then trained using the sets of training data. The machine learning model is trained to produce as output an indication of a predicted material composition in response to receiving an input including one or more texture parameters for a sample of unknown material composition.

In yet another embodiment, the invention provides a laser-based milling system configured to mill a specimen including a plurality of layers, wherein adjacent layers of the specimen are formed of different material compositions. The laser-based milling system includes a femtosecond laser system, an imaging system, and an electronic controller. The femtosecond laser system is configured to controllably etch the specimen and the imaging system is configured to capture image data of the etched surface of the specimen. The electronic controller receives the image data and applies a machine learning model to determine a material composition of the specimen based at least in part on the image data. The electronic controller then continues etching the specimen in response to determining, based on the output of the machine learning model, that the etched surface of the specimen is of a first material composition and stops the etching of the specimen in response to determining, based on the output of the machine learning model that the etched surface of the specimen is of a second material composition. Because a first layer of the specimen is formed of the first material composition and the second layer is formed of the second material composition, determining that the etched surface of the specimen is of the second material composition indicates that the laser system has etched through the first layer and has exposed a second layer of the specimen.

In some implementations, the systems and methods described herein provide an intelligent system that combines a femtosecond pulsed laser with a synchronized CO2injection system that enables fast, clean, high aspect ratio cross-sectioning of microelectronic parts. In some implementations, the system is configured to mill a high aspect ratio trench to expose the buried structures within a microelectronic device (e.g., a CPU IC). Such a trench would not be feasible to mill using traditional FIB methods due to its depth.

In one embodiment, the invention provides a laser-based machining system including a femtosecond laser source, a laser scanning system, a CO2nozzle, a CO2nozzle movement stage, and an electronic controller. The laser scanning system is configured to direct a laser beam from the laser source to a surface of a sample and to controllably adjust a location of a laser spot where the laser beam contacts the surface of the sample. The CO2nozzle is configured to emit a CO2jet and the CO2nozzle movement stage is configured to controllably adjust a location of a CO2spot where the CO2jet contacts the surface of the sample by adjusting a position of the CO2nozzle relative to the sample. The electronic controller is configured to control the laser scanning system to cause the laser spot to follow a defined machining path on the surface of the sample and to control the CO2nozzle movement stage to cause the CO2spot to follow the defined machining path on the surface of the sample. The movement of the CO2spot is controllably synchronized with the movement of the laser spot.

DETAILED DESCRIPTION

System and Method for Predicting Material Composition of a Specimen

The systems and methods described herein provide, among other things, techniques for prediction of material composition of a specimen using a combination of femtosecond pulsed laser and confocal imaging as well as an AI model (e.g., a neural network) trained by the surface metrics extracted from confocal images of lasered samples. In various implementations, the systems and methods described herein provide a fast and reliable, yet affordable solution for material characterization that is much needed for proper machining of microelectronic parts for conducting inspection, failure analysis and reverse engineering. Further, by integrating the method into an automated system that uses the image processing data as feedback to control the laser system, in some implementations, the systems and methods described herein can provide a mechanism for real time end-pointing and/or parameter tuning that offers new capabilities in terms of sample preparation applications.

FIG.1Aillustrates an example of a system configured to detect the material composition of a specimen. The system includes a laser scan head101and a confocal microscope scan head103. The laser scan head101receives a femtosecond-pulsed laser beam from a laser source and controllably directs the laser beam towards a surface of a specimen107. The laser scan head101is configured to scan the laser beam across the surface of the specimen107to perform a milling of the specimen107. Light reflected from the surface of the specimen107during the milling operation is directed by a one-way reflective optic105towards the confocal microscope scan head103. The confocal microscope scan head103captures and directs the captured light from the surface of the specimen to a microscope imaging system configured to capture and record image data of the surface of the specimen107. The confocal microscope scan head103is configured to move relative to the specimen107so that the field of view of the microscope imaging system follows the laser beam as the laser scan head101moves the projected laser beam across the surface of the specimen107.

As illustrated inFIG.1B, a system controller109includes an electronic processor111and a non-transitory computer-readable memory113. The memory113stores data and computer-executable instructions that are accessed and executed by the electronic processor111to provide the functionality of the system controller109(including, for example, the functionality described herein). The system controller109is communicatively coupled to a plurality of movement stages115and is configured to generate and transmit control signals to the movement stages115to control movement of the confocal microscope scan head103and the laser scan head101. The system controller109is also communicatively coupled to the laser source117and is configured to generate and transmit control signals to the laser source117to define and adjust parameters of the laser beam emitted by the laser source117. Similarly, the system controller109is communicatively coupled to the microscope/imaging system119and is configured to receive image data of the surface of the specimen107from the microscope/imaging system119.

FIG.2illustrates an example of a method performed by the system controller109to determine a material composition of the specimen107. First, the system defines laser milling parameters (step201) based, for example, on user input and/or stored data. The laser source117and the movement stages115of the laser scan head101are operated by the system controller109to apply a laser milling to the specimen107(step203). Lasering may be performed using a femtosecond (fs) pulsed laser source with, for example, 40 Watt (W) average power, 1053 nanometer (nm) wavelength, and 257 fs pulse width. The laser system includes a galvo scanner to raster the beam and a three-axis stage with sub-micron accuracy for positioning. An F-theta lens with a 70 mm focal distance is attached to the scanner, resulting in an approximately 15-um diameter spot size. As the laser milling is applied, the system controller109receives and records image data of the milled specimen107from the microscope/imaging system119(step205). The system controller109applies one or more post-processing routines to the captured image data (step207) and then analyzes the captured image data to quantify and extract parameter metrics of the specimen surface (step209). A trained AI model is then applied (step211). For example, in some implementations, the trained AI model is configured to receive as input the values of one or more laser scanning parameters and/or one or more surface texture parameters extracted from the captured image data and, in response, to produce as output an indication of a material composition. Based on the output of the AI model, the system controller109identifies the material composition of the specimen surface (step213).

In some implementations, the system controller109is only configured to capture and process the image data in order to identify the material composition of the specimen. In other implementations, the system controller109is configured to use the identification of the material composition as feedback to control/adjust the laser milling based on the identified material at the surface of the specimen107(step215). For example, in some implementations, the system ofFIG.1Aand system controller ofFIG.1Bare incorporated into a laser milling system for accessing and inspecting an interior of a microelectronic device (e.g., an integrated circuit package). The microelectronic device may include multiple different layers, each formed of a different material. In some implementations, the system may be configured to stop the etching process when the laser machining progresses through a first layer and reaches another layer (e.g., an “end-stop” layer). Accordingly, by identifying the material composition at the surface of the specimen, the system controller109is able to detect when the milling process has reached the “end-stop” layer and is configured to stop the milling process in response to detecting the change in material composition. Similarly, in some implementations, the laser milling process can be optimized by adjusting the laser parameters for different types of materials. Accordingly, in response to identifying the material composition at the surface of the specimen, the system controller109can adjust the laser parameters to a set of parameters corresponding to the identified material type. In some implementations, when the system controller109detects that the material composition has changed, the system controller109can similarly change the laser parameters for the continued milling operation.

Table 1 illustrates a list of examples of laser machining parameters that, in some implementations, can be adjusted by the system controller109. Additionally, in some implementations, the trained AI model may be configured to receive some or all of the laser machining parameters listed in Table 1 as input along with quantified surface texture parameters extracted from the captured image data.

TABLE 1Laser Machining ParametersParameterDescriptionEffective spot sizeSpot size at the interfacewith the sample's surfaceEnergy per pulseEnergy per pulse(EPP)Pulse modeNormal/BurstRepetition rateNumber of pulses persecond% X OverlapOverlap betweenconsecutive laser pulsespots in X direction% Y OverlapOverlap betweenconsecutive laser pulsespots in Y directionRedepositionMethod to mitigatecontrol moderedeposition of ablatedmaterial debris onto thesurface (None/Air)Scan patternLaser scanning path onthe surfaceNumber of cyclesNumber of consecutivetimes the surface isscanned with a certainpatternSurface pre-Surface geometry of themachiningsurface prior to the lasermachining experiment
Table 2 illustrates a list of examples of surface texture parameters that, in some implementations, can be extracted and quantified from the captured image data. In some implementations, the AI model is trained to receive some or all of the surface texture parameters listed in Table 2 as input. In other implementations, the AI model may be trained to receive as input other parameters extracted from the captured image data in addition to or instead of those listed in Table 2. Furthermore, although, in some implementations, the AI model is configured to receive as input a combination of laser milling parameters and surface parameters extracted from the image data, in other implementations, the AI model is configured to receive as input only parameter data extracted from the captured image data. In still other implementations, the AI model may be trained to receive the image data itself as input.

TABLE 2Extracted Surface Texture ParametersParametersDescriptionDOCdepth of cutSqroot mean square height of thesurfaceSalfastest decay auto-correlation rateSskskewness of height distributionStrtexture aspect ratio of the surfaceSkukurtosis of height distributionStdtexture direction of the surfaceSpmaximum height of peaksSdqroot mean square gradient of thesurfaceSvmaximum height of valleysSdrdeveloped area ratioSzmaximum height of the surfaceSmrsurface bearing area ratioSaarithmetical mean height of thesurfaceSdcheight of surface bearing arearatioSpddensity of peaksSxppeak extreme heightSpcarithmetic mean peak curvatureVmmaterial volume at a given heightS10z10-point heightVvvoid volume at a given heightS5p5-point peak heightVmpmaterial volume of peaksS5v5-point valley heightVmcmaterial volume of the coreSdaclosed dales areaVvcvoid volume of the coreShaclosed hills areaVvvvoid volume of the valleysSdvclosed dales volumeShvclosed hills volume

FIG.3illustrates an example of an image of the surface of the specimen captured by the system ofFIG.1Aand system controller ofFIG.1Bboth before post-processing is applied (Graph A) and after post-processing is applied (Graph B). In this example, the acquired image data is prepared for the parameter quantification/extraction steps by applying post-processing using Digital surf Mountains software to perform the following sequence of steps: (1) fill non-measured points, (2) remove outliers, (3) level the data, (4) filling non-measured points again, and (5) thresholding to remove foreign objects. Graph A ofFIG.3displays an initial imaged region and Graph B ofFIG.3depicts the final extracted lasered area after post-processing. Surface texture parameters are extracted from the Graph B image data and depth of cut information (i.e., the depth to which the laser cut into the specimen material) is obtained by subtracting the average height of the lasered area from the average height of the non-lasered area.

FIG.4illustrates an example of a method for training the AI model that is used by the system in the method ofFIG.2to identify the material composition of the surface of the specimen107. In the method ofFIG.4, the training data is assembled by applying a plurality of laser milling parameter sets each to a plurality of different material types. First, the laser milling parameter sets are defined (e.g., by user input, automatically generating a plurality of parameter sets, or accessing stored parameter sets from the memory113) (step401). After the laser milling parameter sets are defined, a new specimen is positioned within the laser chamber (step403). The specimen may be positioned onto a vacuum chuck for sample stability. Laser milling is applied according to a first parameter set of the plurality of parameter sets (step405) while image data is captured (step407). A vacuum system may be used to collect debris. A confocal laser scanning microscope (CLSM) may be used to capture the image data. The field of view (FOV) of the microscope may be selected to cover both a laser-machined area and an in-tact area of the specimen. For example, the FOV may be selected as 520 by 520 microns. However, the FOV may be adjusted according to the size of the lasered area.

The captured image data is analyzed to quantify and extract surface texture parameters from the image data (step409). In some instances, post-processing is applied to the captured image data. The quantified and extracted surface texture parameters are then stored to memory along with an indication of the known material composition of the specimen and an indication of the laser machining parameter set used on the specimen. The system then advances to the next parameter set in the plurality of parameter sets (step413) and another new specimen of the same material composition is positioned (step403). Laser milling is then performed on the new specimen of the same material composition using the new parameter set (step405) while image data is captured (step407) and surface texture parameters are extracted from the captured image data (step409). This process is repeated until all of the different parameter sets have been used to machine specimens of the first material composition (step411).

After all of the parameter sets have been used to machine specimens of the first material type, the material type of the specimen is changed (step417) and the process is repeated for specimens of the next material type beginning by resetting the machine system to the first parameter set (step419). When all of the parameter sets have been used to machine specimens of the second material composition type (step411), the material type of the specimen is changed again (step417) until all of the parameter sets have been used to machine specimens of each of the plurality of different specimen types (step415).

After all of the parameter sets have been used to machine specimens of all of the different material compositions, the training data set is complete and the collected data is used to train the AI model (step421). The input of each training data point of the AI model includes lasering and scanning parameters used for lasering the surface, as well as the surface metrics extracted from the confocal images of the lasered surfaces. For example, in the example ofFIG.4, the training data set stored to the memory113includes a set of surface texture parameters extracted from image data in each experiment, an indication of the material composition of the specimen, and an indication of the laser machining parameter set used on the specimen in the captured image data. Accordingly, the AI model may be trained to receive as input some or all of the laser machining parameters and some or all of the surface texture parameters extracted from the image data and to produce as output, in response to the provided input, an indication of the known material composition corresponding to the specimen in the image data captured during the training method ofFIG.4. In some instances, redundant parameters are not provided as input to the AI model. For example, the set of parameters “X % overlap”, “scanning speed” and “repetition rate,” may include only two independent quantities such that having two out of three parameters determined, the third quantity will collapse to only one possible value. Therefore, one parameter (e.g., speed) may be excluded from the input parameters for the training data point.

Training parameters may be selected based on capability of distinguishing material type, and among two sets of parameters with comparable capability to distinguish among material types, the set with fewer number of elements is chosen to promote simplicity.

The AI model may be, for example, a five-layer fully connected neural network for predicting the material type from the lasering and scanning parameters and surface metrics. During training, the scanning pattern feature is coded using integer numbers 1 through 6, respectively representing bidirectional lines, cross, multiangle (45°), dot, hexagon, and contour patterns. The material type is coded by numbers 1, 2, and 3, respectively representing Aluminum, Silicon, and Copper. For the rest of the features, the real values of the quantities were used without normalization.

In some instances, the training of the AI model includes the use of one or both of a rectified linear unit activation function or a Softmax function. For example, a rectified linear unit activation function may be used in every layer except for the last layer, and a Softmax function may be used in the last layer. Biases and weights may be initialized randomly to [−1,1] interval. The input dataset may be randomly split into a training dataset and a testing dataset. For example, 80% of the input dataset may be included in the training dataset, and 20% may be included in the testing subset. The AI model may be trained using the training dataset for a predetermined number of epochs (e.g., 1000 epochs). The trained AI model is then used for making predictions on the testing dataset. The architecture of the trained neural network is shown inFIG.5.

After training, the AI model may be applied to data captured for new specimens having unknown material composition. The AI model trained by the method ofFIG.4is configured to receive as input one or more laser machining parameters and one or more surface texture parameters extracted from the captured image data of the machined specimen and to produce, as output, an indication of the predicted material composition of the specimen.

FIGS.5through8illustrate examples of experiments used to train an AI model using the method ofFIG.4, and to then test the trained model by applying the model to other image data. In the illustrated example, a total of 1,679 samples of three different material composition types (i.e., aluminum, silicon, and copper) are used. However, more than 1,679 samples or less than 1679 samples may be used. Additionally, the number of material composition types are not limited to three, and may be more than three or less than three. Aluminum, silicon, and copper are often found within microelectronics, and are typically the driving force behind the resulting finish of failure analysis techniques. However, the material compositions used are not limited to aluminum, silicon, and copper.

In the illustrated examples, the surface texture parameters selected for training the AI model include depth of cut (DOC), roughness (Sq), Max height (Sz), and mean height Sa. However, more or fewer parameters may be selected. An output of each training data point reflects the material type of the lasered sample, which is known based on controlled experiments performed to capture the training data.

FIG.6illustrates example results of the accuracy testing of the trained AI model. For example, of 325 testing trials, 34 are Aluminum, 222 are Silicon, and 69 are Copper. As illustrated inFIG.6, testing accuracies are 70% (24 out of 34) for Aluminum, 97% (216 out of 222) for Silicon, and 100% (69 out of 69) for Copper.FIG.7shows a learning curve for the training and test data.

Table 3 lists example probability values associated with predictions by the AI model for different material types. The data included in Table 3 indicates that all mispredictions are the result of the AI model determining Aluminum instead of Silicon and vice versa. This may be due to the closeness of the atomic numbers of Silicon and Aluminum, 14 and 13 respectively, potentially resulting in similar behavior in interaction with the laser.

TABLE 3Mispredictions and corresponding probabilitiesMaterialpAlpSipCuAl0.480.520.00Al0.001.00.00Al0.001.00.00Al0.001.00.00Si0.990.010.00Si0.990.010.00Si0.900.100.00

The depth of cut and Sq values for one set of experimental conditions have been reported for each of the three material types used in this experiment in Table 4.

Although a rather large FOV (520 μm×520 μm) is chosen for analysis in this example, the analysis area can be significantly shrunken in other implementations to increase the resolution of the method in distinguishing multiple types of material that are present in one layer. The limit on the extent of such shrinking would be enforced by the lateral resolution of the confocal microscope which is sub-micron. Therefore, in principle, the area for analysis can be 500 to 1000 times smaller, in each lateral dimension, than what is currently demonstrated.

Additionally, another experiment was performed to demonstrate whether depth of cut alone can distinguish different material types. The ability to do so enables some systems and implementations to utilize a simple height sensor setup and a feedback system for end-pointing/parameter tuning (e.g., instead of a more complex microscope/imaging system). For this example, a second AI model may be trained on a smaller subset of data from the original set of experiments, and the second AI model is applied to other data to test the accuracy of the trained AI model. The results are illustrated in the prediction table ofFIG.8and demonstrate that the depth of cut alone can accurately predict copper, but is less accurate in distinguishing aluminum and silicon. However, increasing the training data may improve the accuracy of an AI model that is trained on depth of cut alone.

In these examples, along with the lasering/scanning parameters that are set by the user, only four surface texture parameters (DOC, Sq, Sz, and Sa) are utilized in training the AI model to predict the material composition of a specimen. However, in some other implementations, the AI model may be trained using the entire image instead of these derived parameters, which can potentially reveal more information about the surface and thus provide more accurate predictions. Additionally, although the examples described herein utilize a confocal microscope imaging system, other implementations may utilize different imaging modalities including, for example, scanning electron microscopy. Finally, in the example described above, the AI model is trained to identify a material composition of a specimen as a single material type (e.g., silicon, copper, or aluminum). In other implementations, the AI model may be trained to identified mixed-material compositions.

System and Method of Laser Machining Specimens with CO2Gas Assisted Processing

A CO2gas delivery system can be used in tandem with laser processing. Laser processing can include laser machining, laser surface texturing, laser scribing, and laser milling.

This disclosure provides a significant increase in efficient particle removal and prevention of redeposition through the timing and location of the laser drilling pulse relative to the CO2pulse. The removal saves the end user a significant amount of time when it comes to both the building process and the actual processing time of the material. This also reduces the amount of original material needed for processing, saving money and allowing one-of-a-kind samples to be used. The disclosure enables pristine laser finishes which can be applicable to laser cross-sections, a new application with a large impact in failure analyses.

In various embodiments, a laser cross-sectioning method and system is provided. The laser cross-sectioning method and system address the throughput challenges associated with focused-ion beam (FIB) methods in combination with precision challenges associated with mechanical and chemical etching methods. In various embodiments, the laser cross-sectioning method and system include a femtosecond laser assembly and a targeted CO2gas delivery system. In various embodiments, the combination of the femtosecond laser assembly and the targeted CO2gas delivery system provides redeposition control and beam tail curtailing, and a hard mask that provides a smaller effective spot which achieves improved qualities in the laser cross-sectioning. The combination of CO2gas injection and hard masking may result in improved high-quality cross sections in comparison to focused-ion beam (FIB) cross sections. In various embodiments, the laser cross-sectioning method and system may provide multiple orders of magnitude of increased speed in comparison to cross-sections prepared by focused-ion beams (FIB) methods. Exemplary laser cross-sectioning methods and systems may provide improved throughput inspections at a significantly reduced cost in comparison to focused-ion beams (FIB).

FIG.9illustrates an example of an intelligent machining system1000that combines a femtosecond pulsed laser with a synchronized CO2injection system that, in some implementations, is configured to provide fast, clean, high aspect ratio cross-sectioning of microelectronic parts. The system1000in this example has been developed for providing high-precision and high-throughput machining for material removal (e.g., for intrusive inspection of electronic components) involving tasks such as sample preparation, delayering, and de-packaging of micro/nano-scale electronics. A laser source1030is positioned to emit a controlled laser beam through a sequence of optical devices (e.g., lenses, filters, and/or mirrors) that deliver the laser beam from the laser source1030to a scan head1050. The scan head1050controllable projects the laser beam to a sample (e.g., a microelectronic device IC) positioned on a sample stage1010.

In some implementations, the laser source1030includes a femtosecond pulsed laser. For example, the laser source1030may include a Coherent Monaco laser system with 40 W average power, 1035 nm wavelength, and 257 femtosecond pulsed width. As illustrated inFIG.10, femtosecond pulsed lasers cause minimal to zero heat affected zone (HAZ) and, therefore, are well-suited for fine machining of microelectronic parts when throughput is also an important consideration.

In the example ofFIG.9, the sample stage1010is configured as a three degree-of-freedom stage (e.g., a Zaber Technologies 3-DOF stage) that has sub-micron translational accuracy for highly precise alignment of the to-be-machined surface with the laser beam and precise focusing/defocusing of the laser beam. As illustrated inFIG.9, the sample stage1010includes a platform (where the to-be-machined sample is placed) positioned on top of a z-stage pillar. The z-stage pillar is configured to controllably adjust a position of the sample platform in the z-direction (e.g., up, and down). The z-stage pillar is coupled to a y-stage track that includes an actuation mechanism (e.g., an electric motor) for adjusting a position of the pillar (and the sample positioned thereon) in the y-direction (e.g., back, and forth). Finally, the y-stage track is coupled to an x-stage track that also includes an actuation mechanism (e.g., an electric motor) for adjusting a position of the y-stage track, the z-stage pillar, and the sample positioned thereon in the x-direction (e.g., left, and right). Accordingly, the sample stage1010is configured to adjustably position a sample on the sample platform in the x, y, and z directions.

The sample stage1010also enables fixed beam laser ablation scheme and further is synchronized with the scan head1050through a machining controller (described below in reference toFIG.11) for implementing hybrid machining in which concurrent movement of the sample stage1010and the scanning head1050will further increase the scanning rate of the machining system1000. The scan head1050is coupled to a support arm track1070configured to adjust a position of the scan head1050in the x-direction. The scan head1050also includes one or more controllable mirrors, lenses, and/or other optical devices configured for controllably lasering and focusing the laser beam (from the laser source1030) on the machining plane (i.e., a target plane corresponding to the sample placed on the sample platform of the sample stage1010). n some implementations, the scan head1050includes a Scanlabs Basicube10 galvo scanner and a Q-optic F-⊖ lens.

In some implementations, the machining system1000ofFIG.9also includes an acousto-optic modulator (AOM) integrated, for example, into the laser source1030or the scan head1050. The AOM is configured to rapidly and controllably shutter the laser beam for enabling the clean movement of the beam from one location to another without damaging the surface. The AOM is used to start and finish the machining process and to enable jumping from one area of the sample to another. In some implementations, the response time of the AOM is faster than 50 ns.

The machine system1000also includes a confocal sensor1130(e.g., a Keyence confocal sensor) positioned adjacent to (or coupled to) the scan head1050and positioned with a downward facing field of view (e.g., aimed at the sample platform of the sample stage1010from above). The confocal sensor1130is configured to collect height information from the surface of the sample positioned on the sample platform of the sample stage1010. This height information is then used as feedback information for tuning the laser machining parameters. In some implementations, because the confocal sensor1130is coupled to the scan head1050, the position of the confocal sensor1130relative to the scan head1050is known. Using this known relationship, the system is able to use the height information captured by the confocal sensor1130to map the surface of the sample in the same coordinate frame used by the scan head1050for the laser machining process.

The machining system1000ofFIG.9includes a CO2nozzle109coupled to the support arm1070by a nozzle extension arm1110. The nozzle extension arm1110is configured to controllably adjust a position of the CO2nozzle1090in the y-direction by extending and retracting the nozzle extension arm1110and to controllably adjust a position of the CO2nozzle1090in the x-direction by moving the nozzle extension arm1110along a track of the support arm1070. In some implementations, the CO2nozzle1090is configured to deliver a CO2gas to the machining area by controllable targeting of the CO2nozzle1090. OAs described in further detail below, in some implementations, the machining system1000may be configured to cause the CO2nozzle1090to follow the same scan pattern as the laser beam. A relatively large spot size of the CO2nozzle1090and the corresponding injection system allows for compensating the lower accuracy of the 2D movement system of the CO2nozzle1090for achieving higher speeds.

When performing laser machining using a femtosecond laser pulse width, the particle size of material removed from the sample during the machining process can range from nanometer-scale to small micrometer-scale. This removed material in many cases redeposits itself onto the surface of the sample itself. This redeposition can then interfere with future processing and can cause many complications including, for example, slowed rate of material removal, limits in depth due to the aspect ratio of the processed area, and difficulty in developing optimized laser and scanning parameters used in the process. In some implementations, air guns (e.g., a nozzle emitting pressurized air) can be used to blow the debris from the surface. However, air guns require particle drag to remove the redeposited particles and, if the size of the particle is less than approximately 5 microns, there cannot exist enough drag force to remove this particle from the surface.

Accordingly, in some implementations, a CO2delivery system associated with the CO2nozzle1090is configured to convert CO2gas to three phases to benefit from unique features of each phase. CO2applied to the sample in the liquid phase eliminates hydrocarbon as it is an excellent solvent. CO2applied to the sample in the gas phase can be used to blow debris from the surfaces of the sample. And CO2in the solid phase (i.e., CO2“snow”) can be controllably applied to the sample surface to remove particle debris generated by the laser machining process that are connected to the sample surfaces by Van der Waal forces. Accordingly, the use of the CO2delivery system in tandem with the laser processing in the machining system1000ofFIG.9provides: (1) enhanced wall quality in the machined cross-sections, (2) improved surface quality in terms of surface roughness, (3) enhanced machined depth, (4) reduced collateral damage (e.g., a reduced or eliminated HAZ), and (5) substantially less permanent (e.g., Van der Waal-bonded) debris/particulates.

FIG.11illustrates an example of a control system for the machining system1000ofFIG.9. A machining controller3010includes an electronic processor3030and a non-transitory computer-readable memory3050. The memory3050stores data and computer-executable instructions that are accessed and executed by the electronic processor3030to provide the functionality of the machining controller3010(including the functionality described herein). The machining controller3010is communicatively coupled to a plurality of electric motors that facilitate the controlled movement of the sample stage1010, the scan head1050, and the CO2nozzle1090. For example, the machining controller3010generates and transmits control signals to an x-motor3070, a y-motor3090, and a z-motor3110of the sample stage1010to control positioning of a sample positioned on the sample platform of the sample stage in 3D space. The machining controller3010also generates and transmits control signals to an x-stage motor3130for the scan head1050to controllably adjust a position of the scan head1050in the x-direction and to one or more additional electric motors3150that control the positioning/orientation of the mirror(s) of the scan head for controllably scanning the laser beam on the surface of the sample. Finally, the machining controller3010generates and transmits control signals to an x-motor3170and a y-motor3190of the nozzle extension arm to control positioning of the CO2nozzle1090in a two-dimensional plane above the sample stage1010.

In this way, the machining controller3010can controllably synchronize movement of the CO2nozzle1090and the projected laser beam relative to the sample to cause the CO2nozzle1090to emit CO2along the same path as the laser machining. The machining controller3010is also communicatively coupled to the laser source1030and the actuators/valves3210of the CO2system and is configured to generate and transmit control signals to regulate the laser beam and the CO2applied to the sample. Accordingly, the machining controller3010is configured to controllably synchronize the location of the laser spot on the surface of the sample in tandem with the CO2jet spot and applies an appropriate time delay between the two to avoid interaction of the laser beam with the CO2spot. In some implementations, the machine controller3010is also configured to generate and transmits control signals to the laser source1030to cause the laser source to adjust various parameters of the laser beam (e.g., on/off, frequency, power/amplitude, pulse width) and to generate and transmit control signals to the actuators and valves3210of the CO2system to control adjust various parameters of the emitted CO2(e.g., on/off, pressure of CO2jet, state of CO2jet, etc.). The machining controller3010is also communicatively coupled to the confocal sensor1130and configured to receive a signal output from the confocal sensor indicative of surface heights of the sample relative to a coordinate frame used by the machining system1000and to adjust the operation of the machining system1000accordingly (e.g., by raising/lowering the platform of the sample stage1010, adjusting an angle of the scan head1050, etc.).

The machining controller3010is also communicatively coupled to a user interface3230including a display device and one or more user input devices (e.g., a keyboard, mouse, etc.). The user interface3230is configured to receive various operating instructions and parameters from a user. In some implementations, the user interface3230includes a computer-assisted design system that is configured to receive inputs from a user defining parameter of the machining to be performed by the machining system1000(e.g., position, size, pattern, depth, etc.). In some implementations, the user interface3230is also configured to receive user instructions defining the state of matter of the CO2to be used during the machining process. In some implementations, the user interface3230allows the user to define different states of matter for the CO2to be used at different stages of the machining process (e.g., the machining process can be user-defined to emit CO2in gas form to “blow” debris at one step and to emit solid CO2snow at another step to remove Van der Waal-bonded debris from the sample).

In some implementations, the fast and precise ablation capability offered by the machining system1000ofFIG.9provides a unique solution for failure analysis practices.FIGS.12A through12Fshow an example of top-down and cross-sectional material removal for an Intel Xeon CPU. Effects of parameter optimization and user of redeposition control techniques are depicted inFIGS.12A through12F. Specifically,FIGS.12A and12Bprovide an overview of the laser processed region of the CPU showcasing the superiority of the proposed technique over FIB with regards to creating deep trenches.FIG.12Bprovides a view of the wall with optimized laser parameters and targeted gas injection system. It can be seen that bulk material removal has taken place through three layers: copper, epoxy, and silicon.FIG.12Cprovides a view of the die layer andFIG.12Dprovides a view of the pillars that are located underneath the die layer, showcasing the ability of the machining system1000to expose buried structures for inspection in a rapid fashion (e.g., minutes vs. days). As is evident from this example, redeposition control technology, which is enabled by the CO2injection system, has dramatically improved the quality of the cut where is has been used versus where is has been absent.FIG.12Eprovides a closer look at the cross section of the die layer andFIG.12Fprovides a closer look at the pillars where redeposition control technology has been applied.FIGS.13A and13Bshow EDS data acquired for the lasered region of the device inFIGS.12A through12F.

The systems and methods described in the examples above provide mechanisms for a tunable approach to laser machining in which a user can customize/tune the laser parameters and the CO2parameters utilized throughout the laser machining process. The system is also configured to synchronize the movement of the laser spot and the CO2spot on the sample to provide controlled clearing of debris from the machining process without undesired interference between the laser beam and the CO2. In various implementations, the machining system1000may be used for machining with or without a “mask” and, in some implementations, the CO2system itself can be operated to create a CO2-based mask on a surface of the sample for the lasering process.

FIG.14illustrates an example of a method for operating the machining system1000to performing laser machining without the use of a mask. The sample is placed on the sample stage1010and a region of interest of the sample is located (step6010). The sample stage1010is operated to adjust the position of the sample and to properly focus the laser beam on the identified region of interest (step6030). CO2is emitted from the CO2nozzle1090to remove debris and/or contaminants from the region of interest (step6050). In some implementations, the position of the CO2nozzle1090is controllably adjusted to “scan” the CO2spot across the region of interest and, in order to increase the degree of cleaning provided, the system can be operated to scan the CO2spot across the region of interest multiple times. The CAD system3230and the targeting system (e.g., data received from the confocal sensor1130) are used to create a shape for marking the region of interest (e.g., the machining to be performed) (step6090).

In some implementations, it is possible to achieve a cross-section with a single laser line and the CO2system greatly reduces aspect ratio issues that would occur with an FIB-based system. In some implementations, utilizing a trapezoidal or other various shapes function better allowing both view of cross-section for imaging and CO2cleaning system. If using a rectangular or trapezoidal shape for the trench, an optical cross-section may be achieved by milling the trench first (step6090) and, depending on the desired depth of the trench, the number of scan cycles can be adjusted. The system is then operated to apply CO2again to remove debris created by the trench milling and to prepare the cross-sectional face for polishing and the final steps (step6110).

The laser system (e.g., the laser source1030and the scan head1050) are then operated to create the CAD-designed marking at the cross-sectional face (step6130). This can be a single laser line or more depending on the desired outcome. For example, depending on the desired depth, the number of cycles or focus of the laser may be adjusted. For this final procedure, CO2is utilized either intermittently or in tandem with the lasering. For intermittent CO2, one or more laser passes are performed (step6130) followed by one or more passes of CO2(step6150). For tandem CO2, the CO2spot, and the laser spot move with each other along the face or, in some implementations, a delay can be set within the CO2system to allow the CO2to lag behind or come before the next laser pulse.

In some implementations, the system is configured to apply a delay between laser passes to remove any condensation that may be present. In other implementations, a separate gas is applied (either through the CO2nozzle1090or through a separate nozzle) to remove condensation between laser passes. For example, the system may be configured to apply a sequence such as one laser cycle (step6130), followed by one CO2cycle (step6150), and then followed by one gas cycle (step6170). This sequence is then repeated until the lasering is complete (step6190). Delays and amounts of passes can all be altered/defined to achieve a desired result (e.g., machining depth, material to be machined, etc.). In some implementations, a chamber, which serves as a control volume, can also be applied with a gas cycle or to remove the need for a gas cycle. As noted above, a simple delay in time between laser passes can remove the need for a secondary gas, but this may increase the total processing time.

Once lasering is complete (step6190), CO2is applied to the sample for a final cleaning (step6210). A single pass of CO2over the sample surface or multiple passes may be used to further remove any debris from the cross-section. As noted above, the state of the CO2applied to the sample for the final cleaning (or for other steps in the machining process) may be adjusted or customized for a particular use depending on the desired outcome. For example, in some implementations, the final cleaning (step6210) includes a first pass where CO2is applied in a gas form, a second pass where CO2is applied in the solid form (CO2snow), and a third pass where CO2is applied in the liquid form.

In the example ofFIG.14, the machining process is performed without the use of a mask. However, the machining system1000can also be operated using a physical mask to assist during the lasering.FIG.15illustrates an example of a machining process performed by the machining system1000using a mask. Again, the sample is placed on the sample stage1010and the region of interest is located (step7010). The position of the sample is adjusted to focus the laser on the region of interest (step7030) and CO2is applied to remove debris and/or contaminants from the region of interest (step7050). The computer-assisted design system3230is used to design a shape for marking (step7070) and then the laser is operated to mill a trench in the sample (step7090) followed by an application of CO2to remove debris formed by the trench milling (step7110).

After the trench is formed and debris is removed, the sample is covered with a mask (step7130). In some implementations, it is important to achieve a tight seal with the mask for optimal results. It is not necessary to refocus the laser to account for the mask—although variations in focus can be made to affect the final cross-sectional area. Covering the sample with the mask prior to trench milling is also possible but may create more material build-up. After the mask is applied, the laser is operated to create the CAD-designed feature at the cross-sectional face (step7150) while CO2is applied intermittently or in tandem with the laser (step7170) as described above in reference to the example ofFIG.14. Once lasering is finishing, the mask is removed (step7190) and CO2is applied to the sample for final cleaning (step7210).

AlthoughFIG.15describes an example in which a physical mask is coupled to the sample prior to the final lasering operation, in some implementations, the machining system1000may be configured to use the CO2system to generate a mask.FIG.16illustrates one such example. As in the previous examples, the sample is positioned on the sample stage1010and the region of interest is located (step8010), the position of the sample is adjusted to focus the laser on the region of interest (step8030), CO2is applied to remove debris/contaminants from the region of interest (step8050), the CAD system3230is used to design a shape for machining (step8070), the laser is operated to mill a trench (step8090), and CO2is applied to remove debris generated by the trench milling (step8110). However, for the final procedure, a constant application of CO2is utilized (step8150) to form a visible hard mask on the top of the sample, which will act as a mask for lasering (step8130). This can be achieved, for example, by (1) applying CO2next to the region of interest allowing the layer to form, but not directly interacting with the incoming laser pulses or (2) applying constant tandem CO2while lasering is being performed (step8130). After lasering is finished the CO2-generated mask is removed and CO2is again applied (step8150) for final cleaning of the sample.

FIG.17is a perspective view of a CO2-assisted laser machining system according to another implementation.FIG.17shows laser scan head9010, confocal height sensor9030, digital microscope9050, XYZ stage9070, laser head9090, gas injection system9110, and cage system beam path and beam expander9130.

During a laser ablation process, some of the ablated material can be re-deposited onto the area that is being lasered. This often leads to sub-optimal laser ablation and poor surface quality due to creation/expansion of the heat affected zone (HAZ). The redeposited material can have various sizes ranging from low or sub micrometer to hundreds of micrometers, and therefore, a cleaning method that can remove both large and small particles are needed. In this implementation, the gas injection system (GIS) based on CO2snow cleaning method is used.

In various embodiments, a CO2snow cleaning method is a dry surface cleaning method in which a high-velocity stream of CO2gas and small dry ice particles (referred to as “snow”) are sprayed onto the surface. The snow is created by controlled expansion of gas or liquid CO2that is propelled through a small orifice right before the nozzle. Particle removal is primarily driven by two mechanisms.

In various embodiments, a first mechanism is the aerodynamic drag force provided by a high velocity CO2stream that exceeds the adhesion force between the particle and the surface. This mechanism is used to remove larger particles and is similar to how high-pressure air or nitrogen gas is used to assist material removal process. However, this mechanism struggles when it comes to small particle removal as the magnitude of the drag force decreases faster than the adhesion forces (e.g., van der Walls, capillary forces, and dipole attraction) with reduction in particle size.

In various embodiments, a second mechanism transfers the momentum from dry ice particles to small particles with diameters in low or even sub micrometer range and provides an improved method and system. In addition, as the CO2snow jet hits the surface, the temperature and pressure increase, which allows the CO2to reach the triple point where gas, liquid and solid CO2can exist simultaneously. In various embodiments, the capability of the CO2snow cleaning method allows the formation of a solid/liquid CO2mask on-demand at desired locations. Furthermore, liquid CO2acts as an excellent hydrocarbon solvent. This can clean the sample and prepare it for SEM and other high vacuum environments after the completion of the lasering process. In various embodiments, the incoming CO2has a temperature of −67° C. which also absorbs heat and aids in the elimination of HAZ during laser processing. In various embodiments, the laser cross-sectioning system has a secondary nozzle that supplies nitrogen gas, any buildup of condensation that the freezing temperatures of the GIS might introduce can be removed on-demand.

FIG.18shows cross-sections of samples cross-sectioned performed with different parameters.FIG.18(at A) shows a cross-section using a traditional laser.FIG.18(at B) shows a cross-section using laser added cycles.FIG.18(at C) shows a cross-section using a laser with gas injection system (GIS). Further,FIG.18shows cross-sections of a microelectronic device that were produced with (i.e., at C) and without the use of CO2(i.e., at A and B) processing, highlighting its effects on the redeposition control.FIG.18(at A) shows a cross-section performed with optimal laser parameters which displays a buildup of redeposition.FIG.18(at B) shows a cross-section performed with ten laser-cutting cycles to cut deeper which led to more redeposition and damage to structure.FIG.18(at C) shows cross-sections performed with the use of a GIS which improved the laser-cutting and eliminated the redeposition and improved the quality of the cross-sectional laser-cut.

FIG.19shows cross-sections of samples cross-sectioned without CO2processing and cross-sectioned with CO2processing.FIG.19(at A) shows cross-sectioning performed without CO2processing which hides subsurface features due to formation of HAZ and melting of the microelectronic device andFIG.19(at B) shows cross-sectioning performed with CO2processing, displaying a wealth of subsurface information.

Without the use of CO2processing, a practical limit remains towards the amount of fluence (i.e., radiant energy received by the surface per unit area for each laser pulse) that can be applied to the sample during processing. Further, the limit about the amount of fluence exists where, at any time, an increase in fluence damages and melts the microelectronic device, thereby ruining the cross-sectional laser cut. In various embodiments, use of CO2removes the limit of fluence by eliminating the HAZ challenge. In various embodiments, the use of CO2processing allows for laser cross-sectioning methods and systems using fluences that are orders of magnitudes above what was previously considered to be a limit. In various embodiments, CO2processing provides for a significant decrease in processing time and results in a much higher quality cross-section using laser cross-sectioning methods and systems provided herein.

FIG.20shows a Gaussian beam profile of the intensity of the laser in relation to the ablation threshold. The typical minimum feature size of a laser system is limited by the laser beam focal spot size which at best is in the order of the beam's wavelength (λ), as dictated by the diffraction limit. The interaction volume of the beam can be larger than the spot size due, in part, to beam tails. Moreover, depending on the material that is interacting with the laser, a portion of the beam energy profile may fall below the ablation threshold and primarily be dissipated in the form of heat. These factors introduce challenges in terms of achieving a high-resolution, high-quality laser cross-section.

In various embodiments, the laser cross-sectioning methods and systems of the disclosure mitigate the challenges of the beam being larger than the spot size and the beam energy profile falling below the ablation threshold, thereby dissipating in the form of heat.

FIG.21shows a schematic depiction of an exemplary masking effect on laser beam tails. In various embodiments, the laser cross-sectioning methods and systems utilize a hard mask to overcome the challenge of the beam being larger than the spot size due, in part, to beam tails. In various embodiments, the physical hard mark is placed on top of the sample (i.e., microelectronic device). In various embodiments, the hard mark blocks and/or shades a threshold portion of the incident beam, effectively removing the beam tails from interacting and effecting the resulting cross-sections of the sample (i.e., microelectronic device).

FIGS.22A and22Bshow a schematic sample orientation in relation to an incoming laser beam and a position of a hard mask. Consideration of how to affix the mask to the surface of the sample (i.e., microelectronic device) was contemplated. A hard mask was contemplated in the form of spin on glass, silver paint, or various stains that are self-adhering to the surface of the sample (i.e., microelectronic device). However, the contemplated hard mask would be non-removable and therefore, will always be present during imaging and, as such, would obscure features of the sample (i.e., microelectronic device).

In various embodiments, to overcome the challenges described above, a removable hard mark was contemplated. In various embodiments, the removable hard mark includes a piece of thin aluminum foil placed on top of the surface of the sample (i.e., microelectronic device). In various embodiments, the size of the piece of aluminum foil is selected such that the remaining areas of the vacuum stage are additionally covered. In various embodiments, the vacuum stage pulls the piece of aluminum foil downward creating a tight seal over the sample (i.e., microelectronic device). In various embodiments, during the lasering process the hard mask is penetrated by any incident beam with high enough fluence, the beam tails lack enough energy to reach the ablation threshold of the hard mask and therefore the top surface of the sample is protected from the beam tails. In various embodiments, the hard mask does not need to be replaced during the laser cross-sectioning process and can be easily removed post-laser cross-sectioning by removing the pull of the vacuum stage. In various embodiments, aluminum foil was selected as the material for the hard mask because it is readily available and inexpensive, and highly reflective of the incident laser beam. Additionally, the choice of the material of the hard mask does not need to be altered based upon any given laser type.

FIG.23shows a schematic depiction of a CO2system interacting with and shielding a sample in conjunction with a laser system. In various embodiments, it was contemplated to utilize the CO2injection mechanism for masking purposes. In various embodiments, the CO2stream is discharged prior to the lasering procedure from the laser system. In various embodiments, the CO2is delivered to the surface at freezing temperatures (i.e., −67° C.), which results in a thin layer of ice build-up on the surface of the sample (i.e., microelectronic device) covering the entire region of interest (ROI). In various embodiments, the thin layer of CO2remains on the surface of the sample as the lasering cycle begins. Upon completion of the laser process, the CO2injection system is turned off and the remaining ice mask can be blown away with compressed air.

FIG.24shows an effect of laser beam tails on an experimental sample.FIG.16(at A) shows the effect of beam tails warping the surface features creating a sloped or curved profile on the top of the surface of the sample as indicated by the arrow in the “side” view.FIG.16(at B) shows the same structure after masking is applied and it was observed that no damage was caused to the top surface features of the sample. In various embodiments, it was observed that enabling the CO2injection system to track the laser and simultaneously create the same pattern as the laser resulted in a more effective mask of the damage caused by the laser beam.

Experimental Data

The described cross-sectioning methods were applied to hierarchically restructured Platinum-Iridium (Pt/Ir) electrodes, that are used in neural interfacing applications. The surface geometry influences the electrochemical performance of electrodes, which is used to manufacture high performance electrodes. An acceptable process can include hierarchical surface restructuring (HSR™). In various embodiments, changes can be made to the subsurface structure that potentially affect the performance and, in some cases, might be detrimental to the integrity of the electrode. Therefore, the ability to rapidly obtain large-area cross-sections of samples, that have undergone the HSR™ process, is important to efficiently arrive at the optimized parameters for HSR™. The subsurface structural features of interest typically range from 0.5 μm to 5 μm. It is observed that a focused ion-beam (FIB) cross-section, spanning a length of only ˜150 μm and a few tens of microns, to capture some of such features takes a minimum of 10 hours. Such a process is prohibitively long when considering that one cross-section must be produced for each restructuring recipe and, as such, thousands of variations must be attempted to arrive at the optimal performance. In various embodiments laser cross-sectioning methods and systems provide a laser cross-section capable of revealing a region of such size in a matter of a few seconds.

Exemplary experiments were conducted to obtain the optimized lasering parameters for the laser cross-sectioning methods and systems. Table 5 provides the resulting optimized values for the parameters along the trends that were observed for each individual parameter. Furthermore, the parameters are ordered based on the impact or priority each has on the final outcome.

TABLE 5SelectedPriorityParameterExplanationvalue1FluenceIncreasing fluence resulted in a more polished cross-80J/cm2(J/cm2)sectional face and faster material removal1aSpot sizeSmaller spot sizes performed better both in terms of8μm(μm)milling rate and surface quality2X (spot) -Higher overlaps produced better quality X-overlap86.36%overlap (%)values of <70% and >90% displayed an artifact socalled as laser induced periodic surface structures(LIPSS)3PatternThe optimal pattern both in terms of milling rate andContoursquality was determined to be a contour pattern, startingfrom the center, and proceeding to the outer boundaryof the defined shape. This pattern leads to an increasein depth of cut and has the unique ability to revealmultiple cross-sections with a single milling process4Pulsed laserIt appears that is X-overlap is kept constant, the10KHzrepetitionoutcome will not be sensitive to the repetition rate.rateHowever, given the limitations in terms of practicingcertain combinations of parameters, repetition rate hadto be kept low, to allow for larger fluences. A lowerrepetition rate was also desirable due to the laserscanning limitations.4aY (line) -Y-overlap shows minimal to zero effect on quality.50%overlap (%)However, <50% Y-overlap results in a reduction inmilling rate.5No. ofIncreasing the number of lasering cycles increases the50laseringcutting depth.cycles

In the order of Table 5 the first parameter is fluence, energy delivered per unit area, given by Equation 1:

Within Equation 1, the value “Epp” represents the energy per laser pulse given in Jules and the value “2ω0” represents the effective laser spot size given in centimeters defined below in Equation 2:

Where the value “ω0” is the beam radius, the value “M” is the beam quality, the value “λ” is laser wavelength, and the value “f” is focal length, and the value “D” is diameter of the entrance beam of the f-theta objective. The spot overlap is defined as Equation 3:

Where the value “vs” is scanning velocity, the value “frep” is the laser repetition rate or how many pulses are delivered to the sample per second. The laser pattern is how the laser spot is scanned across the surface by the galvanometer mirrors with the scan head. The number of cycles refers to how many times the selected pattern is repeated.

FIGS.25A and25Bshow a cross-section of “wet sand” artifact on the left, and another cross-section of “wet sand” on the right depicting a large mitigation of this artifact by increasing the fluence.

Throughout the exemplary experiments, an artifact in more than 70% of the exemplary experiments was observed. The artifact is denoted as “wet sand” and presents itself in a similar texture. It was observed that this can be mitigated with an increase in fluence in conjunction with an increase in the X-overlap of the laser pulses. In addition, with an increase in fluence there was a linear increase in material removal. Importantly, any concerns regarding the formation of heat affected zones (HAZs) and melting that could potentially be caused by high fluence values was eliminated with the use of the CO2gas injection system. As a result of such observations, fluence was determined to be of highest priority in the optimization and recipe building process. Further, due to the inverse relationship between spot size and fluence, it was determined that a smaller spot size was desirable.FIGS.25A and25Bcompares this wet sand artifact at two different fluences.

FIGS.26A,26B, and26Cshow a cross-section of an experimental sample with a laser spot overlap of 60% overlap, 85% overlap, and 92% overlap.FIG.26Ashows a wet sand artifact with 60% overlap,FIG.26Bshows a polished face at 85% overlap, andFIG.26Cshows LIPSS artifact with 92% overlap.

A second common artifact known as laser induced periodic surface structures (LIPSS) was also apparent in many cross-sectioning trials. The parameters that were found to have the most dramatic effect on such phenomenon were X-overlap and the number of lasering cycles. Typically, high X-overlaps are not explored due to the damage they introduce on the surface. This is most often due to the confounding variable of HAZ. However, by leveraging the CO2gas injection system, the HAZ can be eliminated, enabling exploration of higher X-overlap values. It was observed that higher overlap values resulted in a reduction of the wet sand artifact. However, a LIPSS artifact emerged by increasing the X-overlap above 90%. Furthermore, an attempt to polish the face to mitigate such effect with hundreds of lasering cycles appeared to in fact worsen such effect. As a result, it was determined that the optimal outcome can be achieved by increasing the X-overlap up to the onset of the LIPSS formation.FIG.18compares cross-sectioning experiments, resulted from different X-overlap values.

Various lasering patterns were tested for creation of cross-sections. In various embodiments, the laser patterns included sorted lines, bidirectional lines, serpentine, and contour patterns. In various embodiments, it was determined that the optimal pattern both in terms of milling rate and quality was determined to be a contour pattern, starting from the center, and proceeding to the outer boundary of the defined shape, as can be seen inFIG.27(at A). This pattern leads to an increase in depth of cut and has the unique ability to reveal multiple cross-sections with a single milling process.

FIG.28shows SEM micrographs of the hierarchical surface structure of a Pt-10Ir electrode.FIG.28shows the hierarchical surfaces structure that was induced on the surface of a Pt-10Ir alloy electrode used for a paddle-lead spinal cord stimulation electrode array.FIG.28(at B) is a magnification ofFIG.20(at A);FIG.20(at C) is a magnification ofFIG.20(at B); andFIG.20(at D) is a magnification ofFIG.20(at C).

A hierarchal restricted Pt/Ir electrode was used as the sample for conducting the cross-sections. The hierarchical surface structure induced on the surface as a result of restructuring can be observed in the SEM micrographs of the surface of the Pt/Ir electrode targeted for use in a paddle-lead spinal cord stimulation electrode array. The micrographs reveal that the surface hierarchy is notable by a periodic typography comprised of coarse-scale mound-like features that are about several microns wide and 10-15 μm high in size and a finer structure subset on top of the mound-like structures in the range of about a few nanometers to a few hundred nanometers in size. The optimized parameters, as described above, were applied for conducting various laser cross-sectioning. To mitigate the top surface damage, caused by the laser beam tails, CO2gas injection and aluminum foil were applied during the laser cross-sectioning as a masking strategy.

FIG.29shows secondary electron images of laser cross-sections of a control, CO2mask, aluminum foil mask, and CO2in conjunction with an aluminum foil mask. It was observed from the control experiment various tradeoffs of laser cross-sectioning are apparent, more specifically, material redeposition, melting, and top surface damage. It was observed from the CO2masking method results in the reduction of many of the tradeoffs observed from the control experiment. However, it was observed that when only foil was applied as a masking method the cross-sectional face degrades further. It was further observed that the hard mask traps the redeposition within the trench obscuring the cross-sectional face and increases the damage to the sample. Last, it was observed that when combining the CO2and aluminum foil masks an optimal cross-section is obtained with the elimination of the typical laser cross-sectioning shortcomings.

As a result from the exemplary experiments ofFIG.29, it was determined that the combination of hard and CO2masking would result in an optimal process capable of producing cross-sections that are comparable with focused ion-beams (FIB) in quality, yet with material removal rates that are 2,000,000× faster than Gallium focused ion-beams (FIB) and 40,000× faster than traditional lasering.

FIG.30shows results from an exemplary laser cross-sectioning process at various magnifications.FIG.30(at A) shows the cross-sectional face;FIG.30(at B) shows the backscattered electron at A, highlighting the planarity of the cross-sectional face;FIG.30(at C) shows the 2K× magnification at A;FIG.30(at D) shows the 1K× magnification at B;FIG.30(at E) shows the top-down view, highlighting the protection of top surface and the drop-off of the cross-sectional face; andFIG.30(at F) shows the 10KX and 20K× magnifications at B, highlighting the flatness.

FIG.31shows comparative, cross-section SEM images showing results of using focused ion-beams, a control, CO2mask, and CO2in conjunction with an aluminum mask.FIG.31(at A) shows the focused ion-beam FIB;FIG.31(at B) shows the control;FIG.31(at C) shows the CO2mask; andFIG.31(at D) shows the CO2in conjunction with the aluminum mask.FIG.31(at E, F, G, and H) are magnified images ofFIG.31(at A, B, C, and D, respectively).

To quantify the quality of different cross sections using laser and FIB, the surface roughness parameters Sa and Sq were compared. The arithmetic average surface roughness parameter Sa is the average of the absolute values of height deviations of the surface from the base plane. The base plane was selected as the horizontal plane at the average height of the surface, which typically is used and makes the bounded volume above and below this plane equal. The surface roughness parameter Sq is the root mean square of the height deviations from the base plane.

To estimate the height profile of the surface from the SEM images, we used a similar approach. The pixel brightness levels were considered as estimates of the heights, and Sa and Sq were calculated for the cross sections control, FIB, CO2, and CO2with masking at 1500× magnification. The estimated values for Sa and Sq are presented in Table 6.

TABLE 6CO2in conjunction with aluminumControlFIBCO2foil hard maskSa4.424.762.482.36Sq6.305.783.122.98

FIG.32shows various selected regions and their height estimates; from left to right, A is the control, B shows a focused-ion beam (FIB) method, C shows a CO2method, and D shows a CO2method in conjunction with an aluminum hard mask.

Table 7 compares the material removal rates of the proposed method with FIB and traditional lasering. The FIB cross-sectioning took a total 11 hours to reveal a 40 μm-wide face with a depth of 30 μm whereas the proposed laser method with the CO2gas injection system took 10 seconds to reveal a 250 μm-wide face with a 300 μm depth.

Thus, the systems and methods described in the examples above provide, among other things, techniques for laser-based machining accompanied by the application of CO2to the surface being machined wherein the system is configured to synchronizing the movement of the CO2spot on the surface of the sample with the movement of the laser spot projected on the surface of the sample.

Other features and advantages are set forth in the accompanying claims.

Various other components may be included and called upon for providing for aspects of the teachings herein. For example, additional materials, combinations of materials and/or omission of materials may be used to provide for added embodiments that are within the scope of the teachings herein. Adequacy of any particular element for practice of the teachings herein is to be judged from the perspective of a designer, manufacturer, seller, user, system operator or other similarly interested party, and such limitations are to be perceived according to the standards of the interested party.

In the disclosure hereof any element expressed as a means for performing a specified function is intended to encompass any way of performing that function including, for example, a) a combination of circuit elements and associated hardware which perform that function or b) software in any form, including, therefore, firmware, microcode or the like as set forth herein, combined with appropriate circuitry for executing that software to perform the function. Applicants thus regard any means which can provide those functionalities as equivalent to those shown herein. No functional language used in claims appended herein is to be construed as invoking 35 U.S.C. § 112(f) interpretations as “means-plus-function” language unless specifically expressed as such by use of words “means for” or “steps for” within the respective claim.