Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: A method of forming low-k material is provided. The method includes providing a plurality of core-shell particles. The core of the core-shell particles has a first ceramic with a low melting point. The shell of the core-shell particles has a second ceramic with a low melting point and a low dielectric constant. The core-shell particles are sintered and molded to form a low-k material. The shell of the core-shell particles is connected to form a network structure of a microcrystal phase.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: A precursor structure is provided. The precursor structure has the following chemical formula: ( La 2 ? Zr 2 - x ? M x ? O 7 ) · 1 2 ? ( La 2 - y ? M y ? ? O 3 ) , wherein M is a trivalent ion or a pentavalent ion, M? is a bivalent ion, x=0-1, y=0-1.5, and the precursor structure includes a pyrochlore phase. Since the pyrochlore phase may be transformed into the garnet phase through a lithiation process and the phase transition temperature is lower (e.g., 500-1000° C.), the precursor structure may be co-fired with the cathode material (e.g., lithium cobalt oxide (LiCoO2)) to form a thin lamination structure. That is, the thickness of the solid electrolyte may be effectively reduced, thereby improving the ionic conductivity of the solid electrolyte ion battery.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: A method for monitoring and controlling multi-phase condition of reactant in manufacturing process is provided. The monitoring and controlling method includes the following steps. A plurality of clusters of monitoring images is captured corresponding to a plurality of process reaction time points in a plurality of observation regions. According to the clusters of the monitoring images, a plurality of image index features is extracted. According to the image index features, a plurality of phase modes corresponding to the observation regions is determined. According to the image index features, a cluster of generation characteristics of the reactant corresponding to the phase modes is determined. According to the cluster of generation characteristics, an adjustment of the manufacturing process is performed.

Abstract: A first resistivity value and a correlation function relating thickness of a conductive layer having the first resistivity value to a signal from an in-situ monitoring system are stored. A second resistivity value for a conductive layer on a substrate is received. A sequence of signal values that depend on thickness of the conductive layer is received from an in-situ electromagnetic induction monitoring system that monitors the substrate during polishing. A sequence of thickness values is generated based on the sequence of signal values and the correlation function. For at least some thickness values of the sequence of thickness values adjusted thickness values are generated that compensate for variation between the first resistivity value and the second resistivity value to generate a sequence of adjusted thickness values. A polishing endpoint is detected or an adjustment for a polishing parameter is determined based on the sequence of adjusted thickness values.

Abstract: A first resistivity value and a correlation function relating thickness of a conductive layer having the first resistivity value to a signal from an in-situ monitoring system are stored. A second resistivity value for a conductive layer on a substrate is received. A sequence of signal values that depend on thickness of the conductive layer is received from an in-situ electromagnetic induction monitoring system that monitors the substrate during polishing. A sequence of thickness values is generated based on the sequence of signal values and the correlation function. For at least some thickness values of the sequence of thickness values adjusted thickness values are generated that compensate for variation between the first resistivity value and the second resistivity value to generate a sequence of adjusted thickness values. A polishing endpoint is detected or an adjustment for a polishing parameter is determined based on the sequence of adjusted thickness values.

Abstract: A first resistivity value and a correlation function relating thickness of a conductive layer having the first resistivity value to a signal from an in-situ monitoring system are stored. A second resistivity value for a conductive layer on a substrate is received. A sequence of signal values that depend on thickness of the conductive layer is received from an in-situ electromagnetic induction monitoring system that monitors the substrate during polishing. A sequence of thickness values is generated based on the sequence of signal values and the correlation function. For at least some thickness values of the sequence of thickness values adjusted thickness values are generated that compensate for variation between the first resistivity value and the second resistivity value to generate a sequence of adjusted thickness values. A polishing endpoint is detected or an adjustment for a polishing parameter is determined based on the sequence of adjusted thickness values.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: A precursor structure is provided. The precursor structure has the following chemical formula: ( La 2 ? Zr 2 - x ? M x ? O 7 ) · 1 2 ? ( La 2 - y ? M y ? ? O 3 ) , wherein M is a trivalent ion or a pentavalent ion, M? is a bivalent ion, x=0-1, y=0-1.5, and the precursor structure includes a pyrochlore phase. Since the pyrochlore phase may be transformed into the garnet phase through a lithiation process and the phase transition temperature is lower (e.g., 500-1000° C.), the precursor structure may be co-fired with the cathode material (e.g., lithium cobalt oxide (LiCoO2)) to form a thin lamination structure. That is, the thickness of the solid electrolyte may be effectively reduced, thereby improving the ionic conductivity of the solid electrolyte ion battery.

Abstract: A method of forming low-k material is provided. The method includes providing a plurality of core-shell particles. The core of the core-shell particles has a first ceramic with a low melting point. The shell of the core-shell particles has a second ceramic with a low melting point and a low dielectric constant. The core-shell particles are sintered and molded to form a low-k material. The shell of the core-shell particles is connected to form a network structure of a microcrystal phase.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: An apparatus for chemical mechanical polishing includes a platen having a surface to support a polishing pad and an electromagnetic induction monitoring system to generate a magnetic field to monitor a substrate being polished by the polishing pad. The electromagnetic induction monitoring system includes a core positioned at least partially in the platen and a coil wound around a portion of the core. The core includes a back portion and a multiplicity of posts extending from the back portion in a first direction normal to the surface of the platen. The core and coil are configured such that the multiplicity of posts include a first plurality of posts to provide a first magnetic polarity and a second plurality of posts to provide an opposite second magnetic polarity, and the first plurality of posts and the second plurality of posts are arranged in an alternating pattern.

Abstract: A cathode layer and a membrane electrode assembly of a solid oxide fuel cell are provided. The cathode layer consists of a plurality of perovskite crystal films, and the average change rate of linear thermal expansion coefficients of these perovskite crystal films is about 5% to 40% along the thickness direction. The membrane electrode assembly includes the above-mentioned cathode layer, and the linear thermal expansion coefficients of these perovskite crystal films are reduced towards the solid electrolyte layer of the membrane electrode assembly.

Abstract: A method may include depositing a carbon layer on a substrate using physical vapor deposition, wherein the carbon layer exhibits compressive stress, and is characterized by a first stress value; and directing a dose of low-mass species into the carbon layer, wherein, after the directing, the carbon layer exhibits a second stress value, less compressive than the first stress value.

Abstract: A method of controlling processing of a substrate includes generating, based on a signal from an in-situ monitoring system, first and second sequences of characterizing values indicative of a physical property of a reference zone and a control zone, respectively, on a substrate. A reference zone rate and a control zone rate are determined from the first and sequence of characterizing values, respectively. An error value is determined by comparing characterizing values for the reference zone and control zone. An output parameter value for the control zone is generated based on at least the error value and a dynamic nominal control zone value using a proportional-integral-derivative control algorithm, and the dynamic nominal control zone value is generated in a second control loop based on at least the reference zone rate and the control zone rate. The control zone of the substrate is processed according to the output parameter value.

Abstract: A method of controlling polishing includes polishing a substrate at a first polishing station, monitoring the substrate with a first eddy current monitoring system to generate a first signal, determining an ending value of the first signal for an end of polishing of the substrate at the first polishing station, determining a first temperature at the first polishing station, polishing the substrate at a second polishing station, monitoring the substrate with a second eddy current monitoring system to generate a second signal, determining a starting value of the second signal for a start of polishing of the substrate at the second polishing station, determining a gain for the second polishing station based on the ending value, the starting value and the first temperature, and calculating a third signal based on the second signal and the gain.

Abstract: A method may include depositing a mask layer on a substrate using physical vapor deposition, wherein an absolute value of a stress in the mask layer has a first value; and directing a dose of ions into the mask layer, wherein the absolute value of the stress in the mask layer has a second value, less than the first value, after the directing the dose.

Abstract: A method may include depositing a mask layer on a substrate using physical vapor deposition, wherein an absolute value of a stress in the mask layer has a first value; and directing a dose of ions into the mask layer, wherein the absolute value of the stress in the mask layer has a second value, less than the first value, after the directing the dose.