Abstract: To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

Abstract: A semiconductor structure includes alternating stacks of insulating layers and electrically conductive layers which are located over a substrate and are laterally spaced apart from each other by first backside trenches and second backside trenches that are interlaced along a horizontal direction, first backside trench fill structures located in the first backside trenches, and second backside trench fill structures located in the second backside trenches. Each of the first backside trench fill structures includes a respective set of first backside support bridge structures located at a first vertical spacing from the substrate, and each of the second backside trench fill structures includes a respective set of second backside support bridge structures located at a second vertical spacing from the substrate that is different from the first vertical spacing.

Abstract: To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.