Embedded non-volatile RAM technology, and in particular Magneto-resistive RAM (MRAM), continues making great progress in read/write speed and cycling endurance, now rivaling traditional memory technologies. This advancement together with their low-energy consumption and non-volatility opens huge application markets. While stand-alone MRAM products are already being deployed, the first pilot applications for embedded MRAM are just starting to emerge. The availability of Design-for-Test (DFT) tools is key in the transition from low-volume technology exploration and pilot projects to high-volume production. In this paper we explore a new aspect of a Memory Built-In Self-Test (MBIST) tool for eMRAM, namely Error Correcting Code (ECC)-aware test and repair technology. This new MBIST capability provides the ability to make in-system, user-programmable trade-offs concerning the eMRAM repair resources. Having extra control over the repair resources in turn increases both manufacturing yield as well as the longevity of the product in its application.

This paper presents a physics-based modeling framework for the analysis and transient simulation of circuits containing Spin-Transfer Torque (STT) Magnetic Tunnel Junction (MTJ) devices. The framework provides the tools to analyze the stochastic behavior of MTJs and to generate Verilog-A compact models for their simulation in large VLSI designs, addressing the need for an industry-ready model accounting for real-world reliability and scalability requirements. Device dynamics are described by the Landau-Lifshitz-Gilbert-Slonczewsky (s-LLGS ) stochastic magnetization considering Voltage-Controlled Magnetic Anisotropy (VCMA) and the non-negligible statistical effects caused by thermal noise. Model behavior is validated against the OOMMF magnetic simulator and its performance is characterized on a 1-Mb 28 nm Magnetoresistive-RAM (MRAM) memory product.

eMRAM (embedded Magnetoresistive Random Access Memory) is an attractive solution in many non-volatile memory applications because of its small size, fast operation speed, and good endurance. However, due to a relatively small on/off resistance separation, it is a challenge to set an optimal reference resistance to reliably differentiate between a read memory data “1” and “0”. Several trimming circuits are described in the literature to finely adjust a reference resistance value. These circuits are controlled from chip inputs causing time-consuming tests and off-chip engineering analysis. This paper presents a fully automated on-chip trimming process leveraging existing memory BIST (Built-In Self-Test) resources. It analyzes a massive amount of array property data with a minimal number of tests and optimizes the reference trim settings on-chip without the need for any external intervention.

In this paper we present a read circuitry that tackles all STT-MRAM read challenges. First, a negative temperature coefficient (NTC) reference based on an MTJ in series with an “NTC” resistor circuit emulator is described. Then, an offset cancelled voltage sense amplifier using low read current and reference averaging is discussed. Measurement results show a maximum of 2% reference impedance error (vs. ideal) and 1.7% read error rate degradation (vs. technology intrinsic defectivity rate). A 14.7Mb/mm 2 memory density is also achieved, which is the best STT-MRAM published density for embedded applications.

Spin Transfer Torque Magneto-resistive Random-Access Memory (STT-MRAM) is emerging as a promising substitute for flash memories due to scaling challenges for flash in process nodes beyond 28nm. STT-MRAM's high endurance, fast speed and low power makes it suitable for wide variety of applications. An embedded MRAM (eMRAM) compiler is highly desirable to enable SoC designers to use eMRAM instances in their designs in a flexible manner. However, the development of an eMRAM compiler has added challenges of handling multi-fold higher density and maintaining analog circuits accuracy, on top of the challenges associated with conventional SRAM memory compilers. In this paper, we present a successful design methodology for a high density 128Mb eMRAM compiler in a 28nm fully depleted SOI (FDSOI) process. This compiler enables optimized eMRAM instance generation with varying capacity ranges, word -widths, and optional features like repair and error correction. eMRAM compiler design is achieved by evolving various architecture design, validations and characterization methods. A hierarchical and modular characterization methodology is presented to enable high accuracy characterization and industry-standard EDA view generation from the eMRAM compiler.

eMRAM (embedded Magnetoresistive Random Access Memory) has many attractive properties such as small size, fast operation speed, and good endurance. However, MRAM has a relatively small TMR (Tunneling Magnetoresistance) ratio, which means a small on-off state separation. It is a challenge to set an optimal reference resistance to reliably differentiate "1" and "0" states. Several trimming circuits were suggested in the literature to adjust a reference value and its search range. The trim setting can be controlled manually by user input; however, it consumes huge test time and requires off-chip engineering analysis to search and apply a trim setting for an individual memory array. In this paper, we will discuss the recent silicon results of fully automated trim process leveraging existing MBIST (Memory Built-in Self-Test) resources and new features to accommodate more complicated multi-step reference setting implementation through minor update of an existing MBIST circuit. The proposed MBIST solution uses a minimal number of tests to analyze massive array properties and automatically set complicated multi-step trim settings within a chip without the need for an external tester or manual adjustments.

The input-referred offset of a dynamic latch-based sense amplifier for resistive memories is extensively analyzed. This circuit is modeled using both small and large signal analysis, in order to evaluate mismatch effects and to support design robustness to process variations. Effect of various design parameters on offset are studied and reported. It is shown that load capacitance has a profound effect on the sense amplifier offset. Design optimization is then proposed thanks to this analysis, resulting to an input-referred offset (simply called offset in the rest of the paper) down to about 200 Ohms for one sigma variation with 20fF applied load capacitance.

This paper describes an integrated SRAM standby power reduction design in a 40 nm low power process. It features a closed-loop array leakage control with floating bitlines, reducing 46% of leakage current. It relies on self-refreshing virtual VDD clocked by a PVT-compensated SRAM worst-case data retention sensor. The concept is implemented in a 256 kbit SRAM with a 0.242μm2 6T cell.