Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of determining wafer curvature in real-time is presented. The method includes establishing a first temperature profile for a hotplate surface, where the hotplate surface is divided into a plurality of temperature control zones. The method further includes positioning a wafer at a first height above the hotplate surface and determining a second temperature profile for the hotplate surface. The wafer curvature is then determined by using the second temperature profile. Also, a dynamic model of a processing system is presented and wafer curvature can be incorporated into the dynamic model.

Abstract: A method of creating and/or modifying a built-in self test (BIST) table for monitoring a thermal processing system in real-time that includes positioning a plurality of wafers in a processing chamber in the thermal processing system; executing a real-time dynamic model to generate a predicted dynamic process response; creating a measured dynamic process response; determining a dynamic estimation error; determining if the determined dynamic estimation error can be associated with a pre-existing BIST rule in the BIST table; creating a new BIST rule when the dynamic estimation error cannot be associated with any pre-existing BIST rule in the BIST table; and stopping the process when a new BIST rule cannot be created.

Abstract: A method of monitoring a thermal processing system in real-time using a built-in self test (BIST) table to detect, diagnose and/or predict fault conditions and/or degraded performance. The method includes positioning a plurality of wafers in a processing chamber in the thermal processing system, performing a self test process, determining a real-time transient error from a measured transient response and a baseline transient response determined by a BIST rule stored in the BIST table, and comparing the transient error to operational limits and warning limits established by the BIST rule for the self test process.

Abstract: A semiconductor device including: a substrate comprising silicon; a channel region formed on the substrate; a spin injector formed on the substrate at a first side of the channel region and configured to diffuse a spin-polarized current into the channel region; a spin detector formed on the substrate at a second side of the channel region and configured to receive the spin polarized current from the channel region; and a gate formed on the substrate in an area of the channel region.

Abstract: A method of monitoring a single-wafer processing system in real-time using low-pressure based modeling techniques that include processing a wafer 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.

Abstract: A semiconductor device including: a substrate comprising silicon; a channel region formed on the substrate; a spin injector formed on the substrate at a first side of the channel region and configured to diffuse a spin-polarized current into the channel region; a spin detector formed on the substrate at a second side of the channel region and configured to receive said spin polarized current from the channel region; and a gate formed on the substrate in an area of said channel region.

Abstract: 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.

Abstract: 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.

Abstract: A method of monitoring a thermal processing system in real-time using a built-in self test (BIST) table to detect, diagnose and/or predict fault conditions and/or degraded performance. The method includes positioning a plurality of wafers in a processing chamber in the thermal processing system, performing a self test process, determining a real-time transient error from a measured transient response and a baseline transient response determined by a BIST rule stored in the BIST table, and comparing the transient error to operational limits and warning limits established by the BIST rule for the self test process.

Abstract: 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.

Abstract: 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.

Abstract: A method of monitoring a single-wafer processing system in real-time using low-pressure based modeling techniques that include processing a wafer 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.

Abstract: 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.