Abstract: An analysis device includes learning circuitry configured to perform machine learning using a time series data group measured in association with a processing of an object in a processing space and to calculate a value indicating a relationship of time series data in a corresponding time range between respective measurement items and evaluation circuitry configured to evaluate an unknown condition of the processing space based on the value indicating the relationship calculated by performing machine learning by the learning circuitry using a time series data group measured in association with a processing of the object under a known condition of the processing space.

Abstract: An etching method includes providing a substrate on a substrate support in a chamber of a plasma processing apparatus. The etching method further includes etching the substrate with plasma generated in the chamber, thereby forming a recess in the substrate. In the etching, an electrical bias is supplied to the substrate support, to attract ions from the plasma into the substrate. In the etching, at least one of a bias frequency, which is a reciprocal of a time length of a waveform cycle of the electrical bias, and a pulse duty ratio of a pulsed electrical bias is modified to maintain an ion energy flux to the substrate.

Abstract: A disclosed etching method includes (a) forming a protective film containing carbon on a surface in a chamber of a plasma processing apparatus. The etching method further includes (b) etching an etch film of a substrate with plasma generated from an etching gas that includes a hydrogen fluoride gas and a hydrofluorocarbon gas within the chamber. The substrate includes the etch film, which is a silicon-containing film, and a mask containing carbon and provided on the etch film.

Abstract: A substrate processing system includes: an acquiring unit that acquires a plurality of types of time-series data for each time when a plasma processing is performed on a substrate; a learning unit that generates a number of learned abnormal value detection models corresponding to a number of the plurality of types of the time-series data by calculating a data density of each of the plurality of types of time-series data acquired in a first phase; and a quantification unit that quantifies a state in a processing space in a second phase by inputting the plurality of types of time-series data acquired in the second phase into the corresponding learned abnormal value detection models, respectively, and calculating a divergence degree from the plurality of types of the time-series data acquired in the first phase.

Abstract: A plasma processing method includes (a) forming a first protective film on a surface of an inner member of a chamber by a first processing gas including a precursor gas that does not contain halogen; and (b) performing plasma processing on a processing target that is carried in inside the chamber by a plasma of a second processing gas after the first protective film is formed on the surface of the member.

Abstract: A disclosed etching method includes (a) forming a protective film on a surface in a chamber. The etching method further includes (b) etching an etch film of a substrate by using hydrogen fluoride within the chamber. The substrate includes the etch film and a mask provided on the etch film. The protective film is formed of the same type of material as a material of the mask.

Abstract: An analysis device includes: a calculation part configured to calculate a degree of deviation of a processing space, in which a plasma process is performed, from a reference condition by inputting, among time-series data groups measured in the processing space, a time-series data group measured in a determination section, which is a predetermined period of time before a control section, into a time-series analysis model; and a specifying part configured to specify a characteristic value for determining control data at a time of the plasma process of a substrate in the control section based on the calculated degree of deviation.

Abstract: An analysis device includes learning circuitry configured to perform machine learning using a time series data group measured in association with a processing of an object in a processing space and to calculate a value indicating a relationship of time series data in a corresponding time range between respective measurement items and evaluation circuitry configured to evaluate an unknown condition of the processing space based on the value indicating the relationship calculated by performing machine learning by the learning circuitry using a time series data group measured in association with a processing of the object under a known condition of the processing space.