The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

An information processing apparatus includes an acquisition unit configured to acquire process information about a substrate process, the process information including process data and a process condition, and a display control unit configured to control a display on a display apparatus based on the process information acquired by the acquisition unit, wherein the display control unit selectively displays, on the display apparatus, a first screen that displays the process data of a lot including a plurality of substrates on a lot-by-lot basis and a second screen that displays the process data of a first lot on a substrate-by-substrate basis, the first lot being a lot designated by a user from the lot displayed on the first screen.

A server for knowledge recommendation for defect review. The server includes a processor electronically coupled to an electronic storage device storing a plurality of knowledge files related to wafer defects. The processor is configured to execute a set of instruction to cause the server to: receive a request for knowledge recommendation for inspecting an inspection image from a defect classification server; search for a knowledge file in the electronic storage device that matches the inspection image; and transmit the search result to the defect classification server.

A self-contained metrology wafer carrier systems and methods of measuring one or more characteristics of semiconductor wafers are provided. A wafer carrier system includes, for instance, a housing configured for transport within the automated material handling system, the housing having a support configured to support a semiconductor wafer in the housing, and a metrology system disposed within the housing, the metrology system operable to measure at least one characteristic of the wafer, the metrology system comprising a sensing unit and a computing unit operably connected to the sensing unit. Also provided are methods of measuring one or more characteristics of a semiconductor wafer within the wafer carrier systems of the present disclosure.

There are provided a method of inspecting the inspection area and an inspection system thereof. The inspection system comprises an inspection control unit operatively coupled to an inspection tool unit and to a recipe generating unit. The inspection control unit is configured to obtain the design data and the inspection recipe; to provide local segmentation of at least one inspection PoI comprised in an inspection image captured from the inspection area by the inspection tool unit, thereby obtaining inspection structural elements comprised in the at least one inspection PoI, the local segmentation is provided using segmentation configuration data specified in the inspection recipe; to identify one or more target structural elements and design structural elements corresponding thereto, identifying is provided using design association data specified in the inspection recipe; and to enable metrology measurements for the one or more target structural elements using the identified design structural elements.

The present application relates to the field of semiconductor manufacturing technologies, and in particular to a method and an apparatus for automatically processing wafers. The method for automatically processing the wafers includes the following steps: providing several wafers, wherein the wafers operate on a primary path, and the primary path is a path for forming semiconductor structures on the surfaces of the wafers; determining whether there is a need for detecting defects of the wafers, and if yes, automatically switching an operating path of the wafers to a secondary path; detecting the defects of the wafers in the secondary path; and determining whether the defect detection on the wafers is finished, and if yes, automatically switching the operating path of the wafers to the primary path. The application makes it possible to automatically detect the defects of the wafers with different SWR conditions, thereby improving the automation degree of machines.

Embodiments of the present disclosure provide systems and methods for enhancing the semiconductor manufacturing yield. Embodiments of the present disclosure provide a yield improvement system. The system comprises a training tool configured to generate training data based on receipt of one or more verified results of an inspection of a first substrate. The system also comprises a point determination tool configured to determine one or more regions on a second substrate to inspect based on the training data, weak point information for the second substrate, and an exposure recipe for a scanner of the second substrate.

The present disclosure provides a system and a method for controlling a semiconductor manufacturing apparatus. The system includes an inspection unit capturing at least one image of a wafer, a sensor interface generating at least one input signal for a database server, and a control unit. The control unit includes a front-end subsystem, a calculation subsystem, and a message and tuning subsystem. The front-end subsystem receives the at least one input signal from the database server and performs a front-end process to generate a data signal. The calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether damage marks have been caused by the semiconductor manufacturing apparatus and to generate an output signal. The message and tuning subsystem generates an alert signal and a feedback signal according to the output signal and transmits the alert signal to a user.

A recipe verifying method, a recipe verifying server, and a smart manufacturing controlling system using the same are provided. The recipe verifying method includes the following steps. An inputting recipe having a plurality of inputting parameters is intercepted. The inputting recipe is transmitted from one of the inspection apparatuses. A target best known method (BKM) recipe is searched out from a plurality of candidate BKM recipes according to the inputting recipe. A plurality of predetermined limitations are obtained according to the target BKM recipe. Whether the inputting parameters of the inputting recipe meet the predetermined limitations is determined. An error report is generated, if the inputting parameters of the inputting recipe do not meet the predetermined limitations. At least one error is heighted in the error report.

A substrate processing apparatus includes an operating unit for transmitting apparatus data to a memory, the apparatus data being required while a recipe for processing a substrate is executed; and a data matching unit for comparing the apparatus data stored in the memory. When an error occurs in the substrate processing apparatus, the operating unit transmits data representing the error to the data matching unit. The data matching unit includes: a selection unit for selecting first apparatus data which was acquired when the recipe was executed without an occurrence of the error, and stored in the memory; an acquisition unit for acquiring first and second apparatus data from the memory, the first apparatus data being acquired when an error did not occur and the second apparatus data being acquired when an error occurred; and a calculation unit for comparing the first and second apparatus data and calculating a difference therebetween.