Sandisk Technologies Inc.

Kei Samura

Masaaki Higashitani

Yusuke Ikawa

Tsuyoshi Sendoda

Yanli Zhang

Cheng-Chung Chu

Nagarjuna Asam

Seyyed Ehsan Esfahani Rashidi

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.

Modelling and prediction of virtual inline quality control in the production of memory devices

Virtual quality control interpolation and process feedback in the production of memory devices

Virtual metrology for feature profile prediction in the production of memory devices

Growing community of inventors

Yokohama, Japan

Kei Samura