The show must go on: a reliability assessment platform for resistive random access memory crossbars

Resistive random access memory (ReRAM) holds promise for building computing-in-memory (CIM) architectures to execute machine learning (ML) applications. However, existing ReRAM technology faces challenges such as cell and cycle variability, read-disturb and limited endurance, necessitating improvements in devices, algorithms and applications. Understanding the behaviour of ReRAM technologies is crucial for advancement. Existing platforms can either only characterize single cells and do not support CIM operations, or lack a comprehensive software stack for simple system integration. This article introduces NeuroBreakoutBoard (NBB), a versatile, integrable and portable instrumentation platform for ReRAM crossbars. The platform features a software stack enabling experiments via Python from a host PC. In a case study, we demonstrate the capabilities of NBB by conducting diverse experiments on TiN/Ti/HfO₂/TiN cells. Our results show that NBB can characterize individual cells and perform CIM operations with a relative measurement error below 2%.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.