Memory failure prediction

An apparatus, comprising:

• memory storing instructions;
• and
• at least one processor configured via the instructions to:
• receive a sequence of images generated according to a physical layout of a plurality of memory cells in a region of an integrated circuit die to show progress of memory failures in the region of the integrated circuit die, wherein a first image in the sequence images is indicative of a first memory cell of the plurality of memory cells not having failed and wherein a second image in the sequence of images is indicative of the first memory cell having failed;
• train, using a machine learning technique, an artificial neural network to recognize graphical features in the images and to predict, based on a recognized graphical feature in the first image in the sequence, one or more memory failures shown in the second image appearing after the first image in the sequence;
• receive a third image indicative of one or more memory failures in the region of the integrated circuit die;
• and
• identify, based on an analysis of the third image using the artificial neural network, one or more memory cells in the region, the one or more memory cells identified to have memory failures after a time of the third image.

A non-transitory computer readable storage medium storing instructions which, when executed by a microprocessor in a computing device, causes the computing device to perform a method, comprising:

• receiving first data identifying memory failures in a region of an integrated circuit die in a period of time;
• receiving second data identifying a physical layout of a plurality of memory cells in the region;
• generating, based on the first data and the second data, a sequence of images indicative of progress of memory failures in the region of the integrated circuit die, wherein a first image in the sequence images is indicative of a first memory cell of the plurality of memory cells not having failed and wherein a second image in the sequence of images is indicative of the first memory cell having failed;
• training, using a machine learning technique, an artificial neural network to recognize graphical features in the images and to predict, based on a recognized graphical feature in the first image in the sequence, one or more memory failures shown in the second image appearing after the first image in the sequence;
• receiving third data identifying one or more memory failures in the region of the integrated circuit die;
• generating, according to the physical layout, a third image showing the one or more memory failures in the region of the integrated circuit die;
• analyzing, using the artificial neural network, the third image;
• and
• identifying, based on the analyzing, one or more memory cells in the region, the one or more memory cells identified to have memory failures after a time of the third image.