Abstract: Approaches for reducing substrate thickness variation of a substrate are disclosed. One method includes receiving a first etching amount for a plurality of zones of a first substrate when a first thermal application is being provided to the first substrate, determining a mean target thickness of a second substrate, and determining a second etching amount for each of a plurality of zones of the second substrate when a secondary thermal application and the first thermal application are being provided. The method may further include determining an etch correction amount for each zone of the second substrate, and generating a temperature map based on the etch correction amount for each zone of the second substrate, wherein the temperature map indicates a temperature change for each zone of the plurality of zones. The method may further include generating an etching recipe based on the temperature map for etching the second substrate.

Abstract: Embodiments described herein generally relate to electronic devices and electronic device manufacturing. More particularly, some embodiments of the present disclosure provide methods of manufacturing memory devices, for example, dynamic random-access memory cells with buried word-lines. In an embodiment, a method of manufacturing an electronic device is provided. The method includes recessing a metal layer to a first predetermined depth to form a recessed metal layer. The metal layer at least partially fills each feature of a plurality of features formed on a substrate and each feature has a feature depth. The method further includes exposing the recessed metal layer to a carbon-containing plasma to form a metal-carbide layer on the recessed metal layer. The method further includes recessing the recessed metal layer to a second predetermined depth by etching the metal-carbide layer and the recessed metal layer.

Abstract: A method of selectively etching a hardmask layer formed on a device substrate. The method includes supplying an etching gas mixture to a process region of a processing chamber, where a device substrate is disposed in the process region when the etching gas mixture is supplied to the process region, where the device substrate may include a substrate, at least one cavity formed in the substrate, and a hardmask layer formed over the at least one cavity and over the substrate. The method also includes providing radio frequency (rf) power to the etching gas mixture to form a plasma in the process region.

Abstract: A method for selective barrier metal etching includes performing a hydrogen implantation process, in an inductively coupled plasma (ICP) etch chamber, to chemically reduce an oxidized portion of a barrier metal layer formed within a feature in a metal layer on the barrier metal layer, and performing an etch process, in the ICP etch chamber, to remove the hydrogen implanted portion of the barrier metal layer.

Abstract: A method for forming a metal containing feature includes performing a deposition process, the deposition process comprising conformally depositing an over layer on top surfaces of a patterned mandrel layer and over a spacer layer on sidewalls of the patterned mandrel layer, and performing an etch process, the etch process comprising removing the over layer from the top surfaces of the patterned mandrel layer and shoulder portions of the spacer layer, and removing the shoulder portions of the spacer layer, using a fluorine containing etching gas.

Abstract: A method for patterning a boron-containing hard mask includes patterning an oxide hard mask formed on a boron-containing hard mask, and patterning the boron-containing hard mask using the patterned oxide hard mask, wherein the oxide hard mask comprises silicon oxide (SiO2), the boron-containing hard mask is doped with one or more metal elements, and the patterning of the boron-containing hard mask comprises etching the boron-containing hard mask through openings of the patterned oxide hard mask using an etching gas mixture comprising chlorine (Cl2), hydrogen bromide (HBr), and oxygen (O2).

Abstract: Introduced here is an approach to further refining a set of locations that can serve as inputs to a machine learning algorithm. These locations may refer to unique molecular positions in a reference human genome and/or unique mutations thereof that are relevant in diagnosing cancer. A computing system can determine the diagnostic relevance of each location and then discard some of the less diagnostically relevant locations.

Abstract: Methods and compositions for the treatment of a corneal dystrophy in a subject in need thereof are provided. In one aspect, the method includes the step of obtaining a plurality of stem cells comprising a nucleic acid mutation in a corneal dystrophy target nucleic acid from the subject and manipulating the nucleic acid mutation in one or more stem cells of the plurality of stem cells to correct the nucleic acid mutation, thereby forming one or more manipulated stem cells. The manipulated stem cells are isolated and then transplanted into the subject. In some embodiments, the nucleic acid mutation is manipulated using CRISPR system.

Abstract: A method for spacer patterning includes performing a deposition process, the deposition process comprising conformally depositing an over layer over a spacer layer as deposited on top surfaces of a patterned mandrel layer and on sidewalls of the patterned mandrel layer, and performing an etch process using a fluorine containing etching gas. The etch process includes a post-deposition breakthrough process, removing portions of the over layer on the top surfaces of the patterned mandrel layer, and a main-etch process, removing shoulder portions of the over layer and shoulder portions of the spacer layer.

Abstract: Introduced here is an approach to further refining an initial set of target locations that can serve as inputs to machine learning mechanisms. These target locations may refer to unique molecular positions in a reference human genome and/or mutations thereof that are diagnostically relevant for a given cancer type. The system can implement a refinement mechanism to account for unnecessary or problematic data, such as consecutive/overlapping patterns, non-uniform read counts, insufficient data quality, internal processing noises, and/or insufficient data counts.

Abstract: Systems and methods for detecting at least two genomic alleles associated with corneal dystrophy in a sample from a human subject are disclosed in which cells (e.g., epithelial) of the subject are adhered to a tip of a substrate. The tip of the substrate is agitated in a lysis solution that lyses cells adhered to the substrate. The substrate is removed from the lysis solution upon completion of this agitation. The resulting lysis solution is incubated and then genomic DNA from the lysis solution is isolated to form a gDNA solution. From this, identity of at least two nucleotides present in the human TGF?I gene is determined using at least two oligonucleotide primer pairs and the gDNA solution. These at least two nucleotides are located at respective independent positions of the TGF?I gene corresponding to respective independent single nucleotide polymorphisms (SNPs) associated with corneal dystrophy.

Abstract: Introduced here is an approach to further refining a set of locations that can serve as inputs to a machine learning algorithm. These locations may refer to unique molecular positions in a reference human genome and/or unique mutations thereof that are relevant in diagnosing cancer. A computing system can determine the diagnostic relevance of each location and then discard some of the less diagnostically relevant locations.

Abstract: Introduced here is an approach to further refining an initial set of target locations that can serve as inputs to machine learning mechanisms. These target locations may refer to unique molecular positions in a reference human genome and/or mutations thereof that are diagnostically relevant for a given cancer type. The system can implement a refinement mechanism to account for unnecessary or problematic data, such as consecutive/overlapping patterns, non-uniform read counts, insufficient data quality, internal processing noises, and/or insufficient data counts.

Abstract: Introduced here is an approach to detect existence of cancer or a likely onset of cancer based on analyzing DNA data derived from unbounded samples that are not limited to specific locations of a patient’s body or specific types of cancers. One or more machine learning models may be developed using targeted patterns in the human genome. The machine learning models may be trained to analyze and detect mutation patterns characteristic of one or more cancers. The trained models may be used to analyze the unbounded samples to assess the existence cancer or the proximity to the onset of cancer based on identifying mutation patterns in the patient DNA to the patterns characteristic of the one or more cancers.

Abstract: Introduced here is an approach to training a machine learning model to classify a patient amongst multiple cancer types using sets of locations that indicate where mutations typically occur for those multiple cancer types. Upon being applied to genetic information associated with a patient whose health state is unknown, the machine learning model can produce, as input, values that indicate the likelihood of the patient having each of the multiple cancer types. Also introduced here is an approach in which diagnoses are predicted in an improved manner through the application of different models in “tiers” or “stages.” The approach may involve applying a set of multiple models to the genetic information of an individual in order to ascertain the health of the individual, and each of the multiple models can be used to indicate whether the next model in the set should be applied.

Abstract: Introduced here is an approach to training a machine learning model to classify a patient amongst multiple cancer types using sets of locations that indicate where mutations typically occur for those multiple cancer types. Upon being applied to genetic information associated with a patient whose health state is unknown, the machine learning model can produce, as input, values that indicate the likelihood of the patient having each of the multiple cancer types. Also introduced here is an approach in which diagnoses are predicted in an improved manner through the application of different models in “tiers” or “stages.” The approach may involve applying a set of multiple models to the genetic information of an individual in order to ascertain the health of the individual, and each of the multiple models can be used to indicate whether the next model in the set should be applied.

Abstract: A camera captures a display of a gaming device and determines information that appears on the display. The camera is mounted on a video gaming device, and the camera continuously or at various intervals captures images of the screen of the video gaming device. Those images are analyzed to determine information displayed on the video gaming device, such as game speed (e.g., time between handle pulls, total time of play, handle pulls during a session, etc.), bet amounts, bet lines, credits, etc. This information may be determined in various ways, such as by using image processing of images captured by the camera. Machine learning algorithms may also be used to infer key information displayed on the screen of the video gaming device to capture and/or analyze. A housing of the camera may also have a secondary display oriented in a similar direction as the screen of the video gaming device.

Abstract: A method of patterning a substrate is provided. The method includes modifying a surface of a metal-containing layer formed over a substrate positioned in a processing region of a processing chamber by exposing the surface of the metal-containing layer to plasma effluents of a chlorine-containing gas precursor and an oxygen-containing gas precursor to form a modified surface of the metal-containing layer. The method further includes directing plasma effluents of an inert gas precursor towards the modified surface of the metal-containing layer. The plasma effluents of the inert gas precursor are directed by applying a bias voltage to a substrate support holding the substrate. The method further includes anisotropically etching the modified surface of the metal-containing layer with the plasma effluents of the inert gas precursor to form a first recess having a first sidewall in the metal-containing layer.

Abstract: The present disclosure provides forming nanostructures utilizing multiple patterning process with good profile control and feature transfer integrity. In one embodiment, a method for forming features on a substrate includes forming a first mandrel layer on a material layer disposed on a substrate. A first spacer layer is conformally formed on sidewalls of the first mandrel layer, wherein the first spacer layer comprises a doped silicon material. The first mandrel layer is selectively removed while keeping the first spacer layer. A second spacer layer is conformally formed on sidewalls of the first spacer layer and selectively removing the first spacer layer while keeping the second spacer layer.

Abstract: Embodiments described herein generally relate to electronic devices and electronic device manufacturing. More particularly, some embodiments of the present disclosure provide methods of manufacturing memory devices, for example, dynamic random-access memory cells with buried word-lines. In an embodiment, a method of manufacturing an electronic device is provided. The method includes recessing a metal layer to a first predetermined depth to form a recessed metal layer. The metal layer at least partially fills each feature of a plurality of features formed on a substrate and each feature has a feature depth. The method further includes exposing the recessed metal layer to a carbon-containing plasma to form a metal-carbide layer on the recessed metal layer. The method further includes recessing the recessed metal layer to a second predetermined depth by etching the metal-carbide layer and the recessed metal layer.