Abstract: A semiconductor structure and a method for manufacturing the semiconductor structure are provided. The method for manufacturing the semiconductor structure includes: providing a substrate, in which the substrate includes an array area and a peripheral area adjacent to each other, and the array area includes a buffer area connected to the peripheral area; forming a first dielectric layer, a first supporting layer, a second dielectric layer, a second supporting layer and a third dielectric layer, which are successively stacked onto one another, on the substrate,; forming a groove-type lower electrode, which at least penetrates through the third dielectric layer and the second supporting layer, in the buffer area; removing the third dielectric layer through a wet etching process; and etching the second supporting layer in the peripheral area after removing the third dielectric layer.

Abstract: A method for forming a capacitor includes: providing a substrate with an electric contact portion; forming a supporting layer and a sacrificial layer which are alternately laminated on a surface of the substrate, wherein the topmost layer is a supporting layer; forming a capacitor hole penetrating through the supporting layer and the sacrificial layer and exposing the electric contact portion; forming a bottom electrode layer covering an inner surface of the capacitor hole; forming a protective layer covering a surface of the bottom electrode layer; removing the sacrificial layer, during which the bottom electrode layer being protected by the protective layer; removing the protective layer; and sequentially forming a capacitor dielectric layer and a top electrode layer.

Abstract: A method of constructing a decision model includes: obtaining a rule template data and extracting each variable object and each template sample from the rule template data; clustering and analyzing the variable objects to obtain a clustering result; matching the clustering result with each template sample according to the rule template data, and serving the matched clustering result as a first feature; calculating a black sample probability for each variable object and serving the black sample probability of each variable object as a second feature; and constructing the decision model according to the first feature and the second feature.