Abstract
The reliability issues bring great challenges in the performance maintenance of computation-in-memory (CIM), especially based on large-scale resistive random access memory (RRAM) arrays. In this article, we directly characterize the retention of output differential/accumulation current for analog RRAM-based CIM applications. Different from the conventional concerns on the device-level conductance time-dependent fluctuation, this work focuses on the influence of crossbar-level weighted-sum currents on the accuracy loss over time in the general convolutional and fully connected (FC) networks. This is the first Mb-level long-term retention characterization and evaluation in analog RRAM arrays. Comparing with the simulation accuracy based on the short-term device-level test, the computing accuracy values based on crossbar-level characterizations are improved for about 16.8% and 31.3% at 500 and 1000 min at 125 °C and match well with the measured accuracy, indicating that the crossbar-level retention evaluation is more accurate. This work provides new insights for developing RRAM-based CIM systems with excellent reliability.
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