Tokyo Electron Limited

Kenji Sugishima

Sanjeev Kaushal

Pradeep Pandey

Sukesh Janubhai Patel

Sairam Sankaranarayanan

Anthony Dip

Wolfgang Polak

Raymond Joe

Orion Wolfe

Donthineni Ramesh Kumar Rao

Swaroop Ganguly

Robert Filman

David Smith

Jessie Burger

Sundar Gandhi

Performance of a manufacturing tool is optimized. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.

Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool

An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Knowledge generated and accumulated in the autonomous learning system(s) can be cast into semantic networks that can be employed for learning and driving tool goals based on context.

Autonomous biologically based learning tool

System(s) and method(s) are provided for adjustment and analysis of performance of a tool through integration of tool operational data and spectroscopic data related to the tool. Such integration results in consolidated data that enable, in part, learning at least one relationship amongst selected portions of the consolidated data. Learning is performed autonomously without human intervention. Adjustment of performance of the tool relies at least in part on a learned relationship and includes generation of process recipe parameter(s) that can adjust a manufacturing process in order to produce a satisfactory tool performance in response to implementation of the manufacturing process. A process recipe parameter can be generated by solving an inverse problem based on the learned relationship. Analysis of performance of the tool can include assessment of synthetic performance scenarios, identification of spectroscopic condition(s) that affect performance, and extraction of endpoints based at least on time dependence spectroscopic data.

Tool performance by linking spectroscopic information with tool operational parameters and material measurement information

The subject disclosure relates to automatically learning relationships among a plurality of manufacturing tool parameters as applied to arbitrary semiconductor manufacturing tools and a graphical user interface that is supported, at least in part, by an autonomous learning system. The graphical user interface can create one or more matrixes based on received data and can further generate additional matrices by transforming the one or more matrixes. A series of windows can be output, wherein the series of windows, provide performance analysis that comprises a matching between a focus chamber and a reference chamber. In an aspect, the focus chamber and the reference chamber can be different chambers. In another aspect, the focus chamber and the reference chamber can be the same chamber, which provides analysis of the deterioration in performance of the same chamber over time.

Biologically based chamber matching

Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.

Method and system for detection of tool performance degradation and mismatch

System(s) and method(s) for optimizing performance of a manufacturing tool are provided. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.

An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Similarly, the one or more tools can be deployed recursively, in a bottom-up manner in which an individual autonomous tools is assembled in conjunction with other (disparate or alike) autonomous tools to form an autonomous group tool, which in turn can be assembled with other group tools to form a conglomerated autonomous tool system. Knowledge generated and accumulated in the autonomous learning system(s) associated with individual, group and conglomerated tools can be cast into semantic networks that can be employed for learning and driving tool goals based on context.

Autonomous adaptive system and method for improving semiconductor manufacturing quality

A method and system for non-invasive sensing and monitoring of a processing system employed in semiconductor manufacturing. The method allows for detecting and diagnosing drift and failures in the processing system and taking the appropriate correcting measures. The method includes positioning at least one non-invasive sensor on an outer surface of a system component of the processing system, where the at least one invasive sensor forms a wireless sensor network, acquiring a sensor signal from the at least one non-invasive sensor, where the sensor signal tracks a gradual or abrupt change in a processing state of the system component during flow of a process gas in contact with the system component, and extracting the sensor signal from the wireless sensor network to store and process the sensor signal. In one embodiment, the non-invasive sensor can be an accelerometer sensor and the wireless sensor network can be motes-based.

Method of monitoring a semiconductor processing system using a wireless sensor network

An adaptive real time thermal processing system is presented that includes a multivariable controller. The method includes creating a dynamic model of the MLD processing system and incorporating virtual sensors in the dynamic model. The method includes using process recipes comprising intelligent set points, dynamic models, and/or virtual sensors.

Method and apparatus for monolayer deposition (MLD)

A method for correcting systematic errors in an optical measurement tool in which a first diffraction spectrum is measured from a standard substrate including a layer having a known refractive index and a known extinction coefficient by exposing the standard substrate to a spectrum of electromagnetic energy. A tool-perfect diffraction spectrum is calculated for the standard substrate. A hardware systematic error is calculated by comparing the measured diffraction spectrum to the calculated tool-perfect diffraction spectrum. A second diffraction spectrum from a workpiece is measured by exposing the workpiece to the spectrum of electromagnetic energy, and the measured second diffraction spectrum is corrected based on the calculated hardware systematic error to obtain a corrected diffraction spectrum.

Method of correcting systematic error in a metrology system

An optical measurement system and wafer processing tool for correcting systematic errors in which a first diffraction spectrum is measured from a standard substrate including a layer having a known refractive index and a known extinction coefficient by exposing the standard substrate to a spectrum of electromagnetic energy. A tool-perfect diffraction spectrum is calculated for the standard substrate. A hardware systematic error is calculated by comparing the measured diffraction spectrum to the calculated tool-perfect diffraction spectrum. A second diffraction spectrum from a workpiece is measured by exposing the workpiece to the spectrum of electromagnetic energy, and the measured second diffraction spectrum is corrected based on the calculated hardware systematic error to obtain a corrected diffraction spectrum.

Optical measurement system with systematic error correction

A method of monitoring a processing system in real-time using low-pressure based modeling techniques that include processing one or more of wafers in a processing chamber, calculating dynamic estimation errors for the precursor and/or purging process, and determining if the dynamic estimation errors can be associated with pre-existing BIST rules for the process. When the dynamic estimation error cannot be associated with a pre-existing BIST rule, the method includes either modifying the BIST table by creating a new BIST rule for the process, or stopping the process when a new BIST rule cannot be created.

Method for creating a built-in self test (BIST) table for monitoring a monolayer deposition (MLD) system

A method of monitoring a processing system in real-time using low-pressure based modeling techniques that include processing one or more of wafers in a processing chamber; determining a measured dynamic process response for a rate of change for a process parameter; executing a real-time dynamic model to generate a predicted dynamic process response; determining a dynamic estimation error using a difference between the predicted dynamic process response and the expected process response; and comparing the dynamic estimation error to operational limits.

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Tokyo, Japan

Kenji Sugishima