Resistive random access memory (RRAM) is an emerging memory technology that has the potential to replace dynamic random access memory (DRAM) or FLASH. The current memory technology suffer from scalability issues. RRAM can be used as potential replacement for Flash and DRAM. RRAM stores information using resistance states instead of charge. RRAM is non-volatile memory, power efficient, scalable and compatible with the current CMOS process.
Before RRAM can be commercialized the quality of RRAM devices needs to be guaranteed. For this we need to diagnose RRAM devices. The diagnosis allows us to improve the manufacturing process as well as built defect models for memory testing. The traditional fault models do not incorporate the non-linear behaviour of the RRAM device. As a result, tests are created for the wrong test space resulting in a lower yield and more test escapes. To increase the yield and create more reliable fault models, defect models that incorporate the physical defect of the RRAM device are required. To ensure the manufacturing quality of the RRAM devices and for memory testing of RRAM the characterization and diagnosis of RRAM is required. The electrical characterization of RRAM is cheap, fast and is used to evaluate the performance of RRAM. For diagnosis electrical characterization has not been used before.
This work uses the electrical characterization of RRAM to automatically identify defective RRAM devices which can be used for diagnosis. For the defect identification, the key parameters are determined. To assess the performance of the defect identification algorithms the RRAM devices are labelled manually. In total five methods have been implemented to automatically label defective RRAM devices. A statistical analysis is performed on the RRAM devices. Using the labelled data the devices are compared to a nominal device to identify defects in RRAM. Furthermore, K-means, KNN and a CNN algorithm is applied to RRAM. The classification algorithms allow the automatic identification of defective RRAM devices which can be used for diagnosis.
The metrics used for the statistical analysis are insufficient to accurately identify defective RRAM devices. The nominal device method classifies 81% of the device correctly using the euclidean algorithm. The best performance is obtained for the supervised learning algorithm. K-NN classifies 94% of the cycles correct and 84% of the devices. If the data is not labelled the unsupervised learning algorithm can be used. Kmeans classifies 79.6% of the devices correctly which is slightly worse than K-NN. The CNN classifies 67% of the devices correctly using 20 epochs. However, the CNN has not yet been optimized and needs to be improved. For unlabelled data, the unsupervised learning algorithm should be used and for labelled data the K-NN. The electrical characterization of RRAM devices using machine learning looks promising and is much cheaper compared to optical characterization and memory tests. If in the future the defective devices can be linked to the underlying defect, this will lead to cheaper diagnosis of RRAM devices and allow the creation of accurate fault models.