The paper addresses some of the opportunities and challenges related to test and reliability of three major emerging computing paradigms; i.e., Quantum Computing, Computing engines based on Deep Neural Networks for AI, and Approximate Computing (AxC). We present a quantum accelerator showing that it can be done even without the presence of very good qubits. Then, we present Dependability for Artificial Intelligence (AI) oriented Hardware. Indeed, AI applications shown relevant resilience properties to faults, meaning that the testing strongly depends on the application behavior rather than on the hardware structure. We will cover AI hardware design issues due to manufacturing defects, aging faults, and soft errors. Finally, We present the use of AxC to reduce the cost of hardening a digital circuit without impacting its reliability. In other words how to go beyond usual modular redundancy scheme.@en

RRAM density enhancement is essential not only to gain market share in the highly competitive emerging memory sector but also to enable future high-capacity and power-efficient brain-inspired systems, beyond the capabilities of today’s hardware. In this paper, a novel design scheme is proposed to realize reliable and uniform multi-level cell (MLC) RRAM operation without the need of any read verification. RRAM quad-level cell (QLC) capability with 4 bits/cell is demonstrated for the first time. QLC is implemented based on a strict control of the cell programming current of 1T-1R HfO₂-based RRAM cells. From a design standpoint, a self-adaptive write termination circuit is proposed to control the RESET operation and provide an accurate tuning of the analog resistance value of each cell of a memory array. The different resistance levels are obtained by varying the compliance current in the RESET direction. Impact of variability on resistance margins is simulated and analyzed quantitatively at the circuit level to guarantee the robustness of the proposed MLC scheme. The minimal resistance margin reported between two consecutive states is 2.1 kΩ along with an average energy consumption and latency of 25 pJ/cell and 1.65 µs, respectively.

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Memories occupy most of the silicon area in nowadays' system-on-chips and contribute to a significant part of system power consumption. Though widely used, nonvolatile Flash memories still suffer from several drawbacks. Magnetic random access memories (MRAMs) have the potential to mitigate most of the Flash shortcomings. Moreover, it is predicted that they could be used for DRAM and SRAM replacement. However, they are prone to manufacturing defects and runtime failures as any other type of memory. This article provides an up-to-date and practical coverage of MRAM test and reliability solutions existing in the literature. After some background on existing MRAM technologies, defectiveness and reliability issues are discussed, as well as functional fault models used for MRAM. This article is dedicated to a summarized description of existing test and reliability improvement methods developed so far for various MRAM technologies. The last part of this article gives some perspectives on this hot topic.

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