Memristive devices have become promising candidates to complement the CMOS technology, due to their CMOS manufacturing process compatibility, zero standby power consumption, high scalability, as well as their capability to implement high-density memories and new computing paradigms. Despite these advantages, memristive devices are susceptible to manufacturing defects that may cause faulty behaviors not observed in CMOS technology, significantly increasing the challenge of testing these novel devices after manufacturing. This work proposes an optimized Design-for-Testability (DfT) strategy based on the introduction of a DfT circuitry that measures the current consumption of Resistive Random Access Memory (ReRAM) cells to detect not only traditional but also unique faults. The new DfT circuitry was validated using a case study composed of a 3x3 word-based ReRAM with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library. The obtained results demonstrate the fault detection capability of the proposed strategy with respect to traditional and unique faults. In addition, this paper evaluates the impact related to the DfT circuitry’s introduced overheads as well as the impact of process variation on the resolution of the proposed DfT circuitry.

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Memristive devices have become promising candidates to complement and/or replace the CMOS technology, due to their CMOS manufacturing process compatibility, zero standby power consumption, high scalability, as well as their capability to implement high-density memories and new computing paradigms. Despite these advantages, memristive devices are also susceptible to manufacturing defects that may cause different faulty behaviors not observed in CMOS technology, significantly increasing the manufacturing test complexity. This work proposes a Design-for-Testability (DfT) strategy based on the introduction of a on-chip sensor that measures the current consumption of Resistive Random Access Memories (RRAMs) cells to provide the detection of unique faults. The new On-Chip Sensor (ON_CS) was validated using a case study 3×3 RRAM cell array with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library. Experimental results show that the proposed DfT strategy is able to detect not only traditional faults, but also unique faults that can affect RRAM cells. Finally, this paper proposes an DfT strategy that can detect unique faults with an unique operation and can be used during the normal operation of a RRAM.@en

Complementary Metal Oxide Semiconductor (CMOS) technology has been scaled down over the last forty years making possible the design of high-performance applications, following the predictions made by Gordon Moore and Robert H. Dennard in the 1970s. However, there is a growing concern that device scaling, while maintaining cost-effective production, will become infeasible below a certain feature size. In parallel, emerging applications including Internet-of-Things (IoT) and big data applications present high demands in terms of storage and computing capability, combined with challenging constraints in terms of size, power consumption and response latency. In this scenario, memristive devices have become promising candidates to complement the CMOS technology due to their CMOS manufacturing process compatibility, great scalability and high density, zero standby power consumption and their capacity to implement high density memories as well as new computing paradigms. Despite these advantages, memristive devices are also susceptible to manufacturing defects that may cause unique faulty behaviors that are not seen in CMOS, increasing significantly the complexity of test procedures. This paper provides a review about the manufacturing process of memristives devices, focusing on Valence Change Mechanism (VCM)-based memristive devices, and a comparative analysis of the CMOS and memristive device manufacturing processes. Moreover, this paper identifies possible manufacturing failure mechanisms that may affect these novel devices, completing the list of the already known mechanisms, and provides a discussion about possible faulty behaviors. Note that the identification of these mechanisms provides insights regarding the possible memristive devices’ defective behaviors, enabling to derive more accurate fault models and consequently, more suitable test procedures.

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