Apparatus and methods for three dimensional reticle defect smart repair

A method comprising:

• obtaining one or more images of an object on a semiconductor substrate based on a plurality of detectors positioned at respective locations,
• wherein the one or more images of the object includes one or more reticle defect images, and each reticle defect image is an image depicting at least one reticle defect of the object, and the one or more reticle defect images include morphology information and composition information of the object and the at least one reticle defect, and
• wherein obtaining one or more images of the object based on the plurality of detectors positioned at respective locations further includes:
• receiving a first signal reflected from the object and a backscattered electron signal at a first detector positioned opposite of the object;
• analyzing the one or more images of the object on the semiconductor substrate with one or more artificial intelligence assisted object detection algorithms of a control module, wherein the control module includes a training module of training set data an analysis model, memory resources, processing resources, and communication resources, and analyzing the one or more images of the object on the semiconductor substrate including:
• identifying a reticle defect type based on the one or more reticle defect images of the object;
• performing a defect diagnosis by various parameters with the one or more artificial intelligence assisted object detection algorithms of the control module including:
• generating a predicted reticle defect data, wherein the predicted reticle defect data includes a predicted thickness, a predicted composition, a predicted structure, a predicted shape, a predicted size, a predicted roughness, or a predicted length, width, and depth;
• comparing the predicted reticle defect data with a target reticle defects data, wherein the target reticle defects data includes parameters of a defect-free reticle;
• and
• performing a pre-defect repair diagnosis with the control module including determining whether a reticle cleaning process is performed or a reticle repair process is performed;
• when the reticle repair process is determined to be performed in performing the pre-defect repair diagnosis, the reticle repair process is performed including:
• determining a type of gas for repairing the reticle defect type by the control module;
• adjusting process conditions by the control module for a reticle repair process based on the results of analyzing the one or more images of the object on the semiconductor substrate;
• repairing the reticle defect of the object using the adjusted process conditions, wherein the adjusted process conditions are determined to be predictive, and repairing the at least one reticle defect includes selectively supplying the type of gas, wherein the type of gas is at least one of a deposition gas or an etching gas, and wherein, when the type of gas is determined to be the etching gas, the etching gas is diluted in real-time based on at least one of the morphology information and composition information of the at least one reticle defect;
• when the reticle cleaning process is determined to be performed in performing the pre-defect repair diagnosis, the reticle cleaning process is performed including:
• determining a type of cleaning chemical for cleaning the reticle defect type by the control module;
• and
• performing a cleaning recipe on the reticle with the type of cleaning chemical, and wherein the composition information of the object and the at least one reticle defect being obtained from the backscattered electron signal received by the first detector.

A method comprising:

• obtaining one or more images of an object on a semiconductor substrate based on a plurality of detectors positioned at respective locations, the one or more images of the object including one or more reticle defect images, wherein each reticle defect image is an image depicting at least one reticle defect of the object, wherein the one or more reticle defect images include morphology information and composition information of the object and the at least one reticle defect, and the obtaining one or more images of the object based on the plurality of detectors includes:
• receiving a first signal reflected from the object at a first detector;
• receiving a second signal reflected from the object at a second detector;
• receiving a third signal reflected from the object at a third detector;
• and
• receiving a fourth signal reflected from the object and a backscattered electron signal at a fourth detector;
• analyzing the one or more images of the object on the semiconductor substrate with one or more artificial intelligence assisted object detection algorithms of a control module, wherein the control module includes a training module of training set data, an analysis model, memory resources, processing resources, and communication resources, and analyzing the one or more images of the object on the semiconductor substrate including:
• identifying a reticle defect type based on the one or more reticle defect images of the object;
• performing a defect diagnosis by various parameters with the one or more artificial intelligence assisted object detection algorithms of the control module including:
• generating a predicted reticle defect data, wherein the predicted reticle defect data includes a predicted thickness, a predicted composition, a predicted structure, a predicted shape, a predicted size, a predicted roughness, or a predicted length, width, and depth;
• comparing the predicted reticle defect data with a target reticle defects data, wherein the target reticle defects data includes parameters of a defect-free reticle;
• performing a pre-defect repair diagnosis with the control module including determining whether a reticle cleaning process is performed or a reticle repair process is performed;
• when the reticle repair process is determined to be performed in performing the pre-defect repair diagnosis, the reticle repair process is performed including:
• determining a type of material for repairing the reticle defect type by a control module, wherein the control module includes training module of training set data, analysis model, memory resources, processing resources, and communication resources;
• adjusting process conditions by the control module for a reticle repair process based on the results of analyzing the one or more images of the object on the semiconductor substrate;
• and
• repairing the at least one reticle defect of the object using the adjusted process conditions, wherein the adjusted process conditions are determined to be predictive, and repairing the at least one reticle defect includes selectively supplying the type of gas, wherein the type of gas is at least one of a deposition gas or an etching gas, and wherein, when the type of gas is determined to be the etching gas, the etching gas is diluted in real-time based on at least one of the morphology information and composition information of the at least one reticle defect;
• when the reticle cleaning process is determined to be performed in performing the pre-defect repair diagnosis, the reticle cleaning process is performed including:
• determining a type of cleaning chemical for cleaning the reticle defect type by the control module;
• and
• performing a cleaning recipe on the reticle with the type of cleaning chemical, and
• wherein the composition information obtained from the backscattered electron signal, and the backscattered electron signal accounting for at least 40 percent of the morphology and composition information obtained for the object and the at least one reticle defect.