Abstract: A method and apparatus for managing recipes in a semiconductor manufacturing process. Recipes and associated process contexts in a manufacturing execution system and corresponding recipes and associated contexts in a recipe management system are audited to identify discrepancies. The discrepancies are reconciled to ensure that compatible recipes and process contexts are maintained in the manufacturing execution system and the recipe management system. The audit and reconciliation process can be initiated automatically at predetermined time intervals or can be initiated interactively by a user of the manufacturing system.

Abstract: A method for managing recipes in a semiconductor manufacturing process which includes resolving a recipe to provide a resolved recipe using a configuration for a current context and rendering the recipe to provide a rendered recipe. The rendered recipe corresponds to a format suitable for consumption by an equipment interface of the semiconductor manufacturing process.

Abstract: In its various embodiments, the method collects data from process and metrology tools in a semiconductor manufacturing environment, generates statistics from that data, detects tool failures, processing errors, and other conditions that can jeopardize product output, and performs high level process control in the form of tool shutdowns, lot holds, and lot releases. One method as disclosed automates the collection and recording of data from process and metrology tools, automates configuration of data collection, and automates process equipment shut downs, all within the existing framework of existing MES systems and engineering data collection systems. Automation of configurations and data collection is conducted by creation of data collection plans, data collection capability specifications, and other versioned documents within a process control and data collection system as disclosed herein.

Abstract: The functionality of various process control and data collection system embodiments may be improved by employing the database methodology disclosed herein during the requirements-analysis phase for data collection and process control in a semiconductor manufacturing environment. The relational database storage technology as disclosed herein consists of a set of interconnected tables, where each table has a field or an amalgamation of fields (primary key) that uniquely identifies each record (tuple) in the table. In addition, the method as disclosed herein utilizes foreign keys, which represent the value of a primary key for a related table.

Abstract: A method is presented wherein an engineering data collection (EDC) subsystem of a process control system as embodied is responsible for configuring data collection, distributing collected data, and storing collected data, and is supported through underlying interfaces in a transaction and performance monitoring system. In an embodiment, an engineering data collection (EDC) broker accepts data from equipment interfaces or a tap subsystem, then distributes the data to all subscribers such as data history, the engineering data analysis (EDA) system interface, and the statistical process control (SPC) subsystem as disclosed herein. Engineering data analysis system interface is responsible for transmitting raw data and process control system data to engineering data analysis system. Data history subscribes to all data and stores it in the transaction and performance monitoring system's online transaction processing (OLTP) database.

Abstract: The statistical process control (SPC) subsystem is the heart of the data collection and process control system for semiconductor manufacturing in the various embodiments as disclosed herein. SPC is responsible for performing calculations to generate statistics on collected measurement data, applying rules to the statistics, and executing error actions when those rules are violated. SPC subsystem plays a role in corrective actions as well by lifting restrictions previously imposed on tools or lots as a result of rule violations. In an embodiment, SPC is supported through underlying transaction and performance monitoring system components. In an embodiment, SPC procedure is a statistical engine that performs what the process control strategy (PCS) describes, hence there is at least one SPC procedure for each PCS. Components in SPC send data to the chart client subsystem as embodied herein, which allows visualization of statistics generated by SPC procedure in graphical chart form via a web browser.