Resistive RAM (RRAM) is a promising technology to replace traditional technologies such as Flash, because of its low energy consumption, CMOS compatibility, and high density. Many companies are prototyping this technology to validate its potential. Bringing this technology to the market requires high-quality tests to ensure customer satisfaction. Hence, it is of great importance to deeply understand manufacturing defects and accurately model them to develop optimal tests. This paper presents a holistic framework for defect and fault modeling that enables the development of optimal tests for RRAMs. An overview and classification of RRAM manufacturing defects are provided. Defects in contacts and interconnects are modeled as resistors. Unique RRAM defects, e.g., forming defects, require Device-Aware defect modeling which incorporates the defect's impact on the device's electric properties by adjusting the affected technology and electrical parameters. Additionally, a systematic approach to define the fault space is presented, followed by a methodology to validate this space. With this methodology, accurate fault modeling for contact, interconnect, and forming defects is performed and tests are developed. The tests are able to detect all faults in a time-efficient manner, thereby proving the effectiveness of the framework. Finally, an outlook on future RRAM testing is presented.

High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.

Fin Field-Effect Transistor (FinFET) technology enables the continuous downscaling of Integrated Circuits (ICs), using the Complementary Metal-Oxide Semiconductor (CMOS) technology in accordance with the More Moore domain. Despite demonstrating improvements on short channel effect and overcoming the growing leakage problem of planar CMOS technology, the continuity of feature size miniaturization allowed by FinFETs tends to increase sensitivity to Single Event Upsets (SEUs) caused by ionizing particles, especially in blocks with higher transistor densities as Static Random-Access Memories (SRAMs). Variation during the manufacturing process has introduced different types of defects that directly affect the SRAM's reliability, such as weak resistive defects. As some of these defects may cause dynamic faults, which require more than one consecutive operation to sensitize the fault at the logic level, traditional test approaches may fail to detect them and test escapes can occur. These undetected faults associated with weak resistive defects may affect the FinFET -based SRAM reliability during the lifetime. In this context, this paper proposes to investigate the impact of ionizing particles on the reliability of FinFET -based SRAMs in the presence of weak resistive defects. Firstly, a TCAD model of a FinFET-based SRAM cell is proposed in order to allow the evaluation of the ionizing particle's impact. Then, SPICE simulations are performed considering the current pulse parameters obtained with TCAD. In this step, weak resistive defects are injected into the FinFET-based SRAM cell. Results show that weak defects may have either a positive or negative influence on the cell reliability against SEUs caused by ionizing particles.

Manufacturing defects can cause faults in FinFET SRAMs. Of them, easy-to-detect (ETD) faults always cause incorrect behavior, and therefore are easily detected by applying sequences of write and read operations. However, hard-to-detect (HTD) faults may not cause incorrect behavior, only parametric deviations. Detection of these faults is of major importance as they may lead to test escapes. This paper proposes a new design-for-testability (DFT) scheme for FinFET SRAMs to detect such faults by creating a mismatch in the sense amplifier (SA). This mismatch, combined with the defect in the cell, will incorrectly bias the SA and cause incorrect read outputs. Furthermore, post-silicon calibration schemes can be used to avoid over-testing or test escapes caused by process variation effects. Compared to the state of the art, this scheme introduces negligible overheads in area and test time while it significantly improves fault coverage and reduces the number of test escapes.

STT-MRAM mass production is around the corner as major foundries worldwide invest heavily on its commercialization. To ensure high-quality STT-MRAM products, effective yet cost-efficient test solutions are of great importance. This article presents a systematic device-aware defect and fault modeling framework for STT-MRAM to derive accurate fault models which reflect the physical defects appropriately, and thereafter optimal and high-quality test solutions. An overview and classification of manufacturing defects in STT-MRAMs are provided with an emphasis on those related to the fabrication of magnetic tunnel junction (MTJ) devices, i.e., the data-storing elements. Defects in MTJ devices need to be modeled by adjusting the affected technology parameters and subsequent electrical parameters to fully capture the defect impact on both the device's electrical and magnetic properties, whereas defects in interconnects can be modeled as linear resistors. In addition, a complete single-cell fault space and nomenclature are defined, and a systematic fault analysis methodology is proposed. To demonstrate the use of the proposed framework, resistive defects in interconnect and pinhole defects in MTJ devices are analyzed for a single 1T-1MTJ memory cell. Test solutions for detecting these defects are also discussed.