Block-based prediction for manufacturing environments

Abstract

Embodiments presented herein provide techniques for executing a block-based (BB) workflow to generate predictions related to a semiconductor manufacturing environment. Embodiments include receiving atleast one BB workflow comprising a plurality of blocks. The plurality of blocks may specify a set of operations for generating the predictions for a specified future time interval. Embodiments include accessing a plurality of block definitions corresponding to the plurality of blocks. Embodiments include executing the at least one BB workflow by performing the set of operations based on the plurality of block definitions, including extracting data from the semiconductor manufacturing environment, the data comprising both static data and dynamic data related to equipment in the manufacturing environment, determining, based on the extracted data, the predictions related to the manufacturing environment for the specified future time interval, and publishing the predictions to at least one component in the semiconductor manufacturing environment.

Description

technical field [0001] Embodiments of the present disclosure relate generally to forecasting, and more particularly to techniques for predicting the future state of equipment and lots in a manufacturing environment using block-based workflows. Background technique [0002] Manufacturing plants in many different industries are responsible for the production of products used in all aspects of life. For example, in the case of semiconductor manufacturing, semiconductor fabrication facilities manufacture products that are ubiquitous in everyday life (eg, microprocessors, memory chips, microcontrollers, and other semiconductor devices). These semiconductor devices are used in a variety of applications, examples of which include automobiles, computers, home appliances, cellular phones, and many others. Furthermore, in recent years, the number of applications and the demand for devices (including semiconductor devices) have steadily increased. This increase in demand has driven m...

AI Technical Summary

Problems solved by technology

However, in the case of predictive models built using custom code, adapting to such changes may require a level of expertise that may not be available to manufacturing facilities (e.g., end users may not have coding experience, etc.), require adequate time support, and require Substantial costs (e.g. due to facility complexity), etc.

Images