A method includes dispensing a first photoresist material onto a first substrate positioned on a substrate stage within a process chamber of a coating apparatus, wherein the process chamber is in a first exhaust rate during the dispensing the first photoresist material; measuring a thickness of the first photoresist material on the first substrate; adjusting an exhaust efficiency within the process chamber through an exhaust assembly based on the measured thickness, wherein the adjustment regulates an evacuation of air and volatiles from the process chamber; dispensing a second photoresist material onto a second substrate positioned on the substrate stage, wherein the process chamber is in a second exhaust rate during the dispensing the second photoresist material.

According to an embodiment of the present invention, a method for processing a substrate through a heater that heats the substrate to perform a semiconductor process, the method comprising: inputting, into a correlation formula of at least one independent variable, which is a parameter related to the heater, and a dependent variable including a measured temperature of the heater, a measurement value corresponding to the independent variable, and calculating a predicted temperature of the heater; and applying a Kalman filter to the predicted temperature to calculate an estimated temperature.

Methods and devices for determining a temperature of a substrate during processing are provided herein. Embodiments include extracting modes from a virtual model of thermal conditions within a processing chamber. Embodiments further include receiving thermal sensor data associated with a target substrate. Embodiments further include using compressed sensing to generate a thermal map for the target substrate based on the thermal sensor data and the extracted modes.

A method and system for optimizing a semiconductor manufacturing process using an artificial intelligence (AI) system comprising machine learning (ML) models trained on mask work datasets. The AI system generates optimized process parameters for a multi-step semiconductor manufacturing process based on an input mask work. The manufacturing process includes photolithography, etching, ion implantation, chemical vapor deposition (CVD), physical vapor deposition (PVD), atomic layer deposition (ALD), thermal oxidation, and/or chemical-mechanical polishing (CMP). During manufacturing, metrology data is collected and input into the ML models to predict end-of-line electrical performance parameters. If the predicted parameters deviate from target values, the AI system adjusts process parameters to optimize performance. The ML models are retrained using the collected metrology data to improve AI system performance over time. The system includes a semiconductor manufacturing apparatus configured to perform the optimized manufacturing process.

A manufacturing method of a semiconductor device capable of improving the accuracy of overcurrent detection is provided. The manufacturing method of a semiconductor device includes a semiconductor wafer testing process includes a first testing process to determine the resistance variation rate of the replica resistor due to manufacturing variations when manufacturing the semiconductor wafer, and a setting process to determine the variation value of the reference current based on the resistance variation rate determined in the first testing process and set the current value of the current circuit to reduce the variation value.

The present disclosure relates to a radio frequency (RF) sensor and a method for monitoring plasma status. The RF sensor includes a collector configured to collect, as sensing data, an induced electromotive force induced during a plasma process; and a processor configured to record the induced electromotive force as a function of time, perform Fourier transformation on the recorded induced electromotive force function to derive an amplitude value of an n-th harmonic (where n is a natural number equal to or greater than 1), and apply the derived amplitude value and a setting value of plasma equipment identified at the time of sensing data generation to an artificial intelligence algorithm to derive prediction data capable of monitoring plasma status and plasma process status. The RF sensor may also be applied in other embodiments.

The present disclosure relates to a system for semiconductor manufacturing, designed for rapid chamber pressure modulation through an array of small valves and pumps. The system incorporates micro shutters to achieve precise and rapid gas flow regulation. A system controller adjusts motor currents to compensate for nonuniformities resulting from incoming substrates and design constraints within the process system, thereby ensuring improved substrate uniformity during semiconductor manufacturing processes.

This disclosure provides methods and systems of processing a semiconductor wafer. One method includes obtaining wafer characterization metrology information of a wafer, the wafer including a plurality of dies, and generating a plurality of predicted die shapes based on the wafer characterization metrology information being input into a computing model. Each of the plurality of predicated die shapes corresponds to one of the plurality of dies of the wafer. The method further includes processing the wafer to obtain the plurality of dies and processing the plurality of dies based on the plurality of predicated die shapes.

Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

A substrate processing system includes a processing chamber, a substrate support, a heat source, a gas delivery system and a controller. The substrate support is disposed in the processing chamber and supports a substrate. The heat source heats the substrate. The gas delivery system supplies a process gas to the processing chamber. The controller controls the gas delivery system and the heat source to iteratively perform an isotropic atomic layer etch process including: during an iteration of the isotropic atomic layer etch process, performing pretreatment, atomistic adsorption, and pulsed thermal annealing; during the atomistic adsorption, exposing a surface of the substrate to the process gas including a halogen species that is selectively adsorbed onto an exposed material of the substrate to form a modified material; and during the pulsed thermal annealing, pulsing the heat source multiple times within a predetermined period to expose and remove the modified material.