An optical manufacturing process sensing and status indication system is taught that is able to utilize optical emissions from a manufacturing process to infer the state of the process. In one case, it is able to use these optical emissions to distinguish thermal phenomena on two timescales and to perform feature extraction and classification so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process. In other case, it is able to utilize these optical emissions to derive corresponding spectra and identify features within those spectra so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process.

An optical manufacturing process sensing and status indication system is taught that is able to utilize optical emissions from a manufacturing process to infer the state of the process. In one case, it is able to use these optical emissions to distinguish thermal phenomena on two timescales and to perform feature extraction and classification so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process. In other case, it is able to utilize these optical emissions to derive corresponding spectra and identify features within those spectra so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process.

A method and system of recycling aluminum alloy rims, the method and system comprising providing a feed of a plurality of aluminum alloy rims of different alloys, for each rim in the feed of aluminum alloy rims, determining a composition of that aluminum alloy rim, determining a plurality of recycled aluminum alloy composition ranges, and dividing the feed of aluminum alloy rims into a plurality of batches of aluminum alloy rims, each batch of aluminum alloy rims in the plurality of batches of aluminum alloy rims corresponding to an associated recycled aluminum alloy composition range in the plurality of recycled aluminum composition ranges, such that each rim in the plurality of aluminum alloy rims is allocated to an associated batch in the plurality of batches of aluminum alloy rims based on the composition of that aluminum alloy rim.

The present invention is enclosed in the area of machine learning, in particular machine learning for the analysis of High or Super-resolution spectroscopic data, which typically comprises analysis of highly complex samples/mixtures of substances and/or data with low resolution, for instance Laser-Induced Breakdown Spectroscopy (LIBS). It is an object of the present invention a method of computational self-learning for characterization of one or more constituents in a sample, from electromagnetic spectral information of such sample, which changes the paradigm associated with prior art methods, by using only sub-optical spectral information, i.e., obtaining the resolution of the spectral information and thereby be able to extract spectral lines—thus determining a spectral line position—from such spectral information, hence avoiding all the uncertainty associated with pixel based methods. It is also an object of the present invention a computational apparatus configured to implement such method.

Disclosed is an operating method of a metal sorting system using laser induced breakdown spectroscopy (LIBS), which may include: analyzing a metal component distribution for various metals using LIBS library information; setting multiple clusters according to the metal component distribution; performing first regression component analysis with respect to spectral data of a metal sample; calculating a probability that the spectral data will belong to each of the set multiple clusters using the first regress component analysis result; performing second regression component analysis with respect to the spectral data which belong to each cluster; and discriminating a type of metal sample by a weighted sum of the calculated probability and the second regression component analysis result.

Disclosed are embodiments of an improved apparatus and system, and associated methods for optically diagnosing a semiconductor manufacturing process. A hyperspectral imaging system is used to acquire spectrally-resolved images of emissions from the plasma, in a plasma processing system. Acquired hyperspectral images may be used to determine the chemical composition of the plasma and the plasma process endpoint. Alternatively, a hyperspectral imaging system is used to acquire spectrally-resolved images of a substrate before, during, or after processing, to determine properties of the substrate or layers and features formed on the substrate, including whether a process endpoint has been reached; or before or after processing, for inspecting the substrate condition.

Disclosed are embodiments of an improved apparatus and system, and associated methods for optically diagnosing a semiconductor manufacturing process. A hyperspectral imaging system is used to acquire spectrally-resolved images of emissions from the plasma, in a plasma processing system. Acquired hyperspectral images may be used to determine the chemical composition of the plasma and the plasma process endpoint. Alternatively, a hyperspectral imaging system is used to acquire spectrally-resolved images of a substrate before, during, or after processing, to determine properties of the substrate or layers and features formed on the substrate, including whether a process endpoint has been reached; or before or after processing, for inspecting the substrate condition.

The present invention provides a composite oxide that can achieve a high low-temperature output characteristic, a method for manufacturing the same, and a positive electrode active material in which the generation of soluble lithium is suppressed and a problem of gelation is not caused during the paste preparation. A positive electrode active material for non-aqueous electrolyte secondary batteries, including a lithium-metal composite oxide powder including a secondary particle configured by aggregating primary particles containing lithium, nickel, manganese, and cobalt, or a lithium-metal composite oxide powder including both the primary particles and the secondary particle. The secondary particle has a porous structure inside as a main inside structure, the slurry pH is 11.5 or less, the soluble lithium content rate is 0.5 [% by mass] or less, the specific surface area is 3.0 to 4.0 [m.sup.2/g], and the porosity is more than 50 to 80 [%].

The present invention provides a composite oxide that can achieve a high low-temperature output characteristic, a method for manufacturing the same, and a positive electrode active material in which the generation of soluble lithium is suppressed and a problem of gelation is not caused during the paste preparation. A positive electrode active material for non-aqueous electrolyte secondary batteries, including a lithium-metal composite oxide powder including a secondary particle configured by aggregating primary particles containing lithium, nickel, manganese, and cobalt, or a lithium-metal composite oxide powder including both the primary particles and the secondary particle. The secondary particle has a porous structure inside as a main inside structure, the slurry pH is 11.5 or less, the soluble lithium content rate is 0.5 [% by mass] or less, the specific surface area is 3.0 to 4.0 [m.sup.2/g], and the porosity is more than 50 to 80 [%].

Disclosed are embodiments of an improved apparatus and system, and associated methods for optically diagnosing a semiconductor manufacturing process. A hyperspectral imaging system is used to acquire spectrally-resolved images of emissions from the plasma, in a plasma processing system. Acquired hyperspectral images may be used to determine the chemical composition of the plasma and the plasma process endpoint. Alternatively, a hyperspectral imaging system is used to acquire spectrally-resolved images of a substrate before, during, or after processing, to determine properties of the substrate or layers and features formed on the substrate, including whether a process endpoint has been reached; or before or after processing, for inspecting the substrate condition.