SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I project will develop novel electric-field-controlled magnetic memory devices, where switching of the nonvolatile memory bits is performed by voltages (i.e. electric fields), rather than spin-polarized currents or magnetic fields, for extremely low energy dissipation applications. These memory devices will be used in a Magnetoelectric Random Access Memory (MeRAM), providing significant advantages in terms of energy efficiency (up to 100x), density (up to 10x), and scalability (down to <10nm) compared to state-of-the-art magnetic memories (MRAM) such as spin transfer torque (STT-MRAM). The improved energy efficiency results from the suppression of Ohmic losses (heating) since no currents are used. This project will focus on development and optimization of magnetic bit designs for MeRAM. Their performance will be tested and compared to identify the most promising candidates for product development.

The broader impact/commercial potential of this project will be in the broad area of advanced low-power electronics. In particular, the high memory density allows for MeRAM to be used as a nonvolatile alternative to replace dynamic random access memory (DRAM, a $37B market), making it nonvolatile and improving its speed, energy efficiency, and scalability. Additionally, the excellent energy efficiency of MeRAM allows it to be integrated with CMOS, potentially resulting in revolutionary new applications in ultralow-power logic for mobile systems on chip (SoC). This will result in instant-on nonvolatile electronics, which can be powered on/off instantaneously without the loss of information. It allows for an entirely new user experience where traditional boot-up and shut-down times are eliminated, and provides energy savings by eliminating standby power, representing a new paradigm in electronics. MeRAM is the only nonvolatile memory technology that meets all the requirements for such applications.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is in addressing the novel hardware challenges of energy and speed associated with big data applications. The electronics industry is quickly transforming from a compute-centric model to a data-centric model, where the key to success in the market place is the ability to store, manipulate and access large amounts of data in a fast and energy-efficient manner. The existing memory technologies cannot meet these challenges. This changing landscape offers a major potential for emerging memory technologies that can successfully address these new emerging demands. The Electric-Field-Controlled Nonvolatile Magnetic Memory technology developed under this project offers the most viable low-cost, low-power, and high-endurance solution to these new demands. Conservative estimates project a rapid growth in the emerging memory technologies market, reaching $2B in 2018. We expect that Electric-Field-Controlled Nonvolatile Magnetic Memory will be able to capture a significant part of this emerging market. More broadly, it can be used in many general computing and information processing systems, and hence it can potentially impact a major part of the $300B semiconductors industry by enabling ultra-low power green and instant-on electronic systems.This Small Business Innovation Research Phase II project will focus on the development of prototype Electric-Field-Controlled Nonvolatile Magnetic Memory chips and arrays. This memory technology uses a fundamentally new way to write information into the nonvolatile memory bits, providing major advantages in terms of power dissipation, cost, density, and scalability. This project will result in chip prototypes, providing significant advantages in terms of energy efficiency, density, and scalability when compared to alternative existing or emerging memory technologies. They also provide virtually unlimited endurance. Circuit design for memory product development will also be performed, as well as modeling in support of circuit development and device optimization.