H. Hachino

Bio: H. Hachino is an academic researcher. The author has contributed to research in topics: Non-volatile memory & Universal memory. The author has an hindex of 1, co-authored 1 publications receiving 921 citations.

A novel nonvolatile memory with spin torque transfer magnetization switching: spin-ram

Abstract: A novel nonvolatile memory utilizing spin torque transfer magnetization switching (STS), abbreviated spin-RAM hereafter, is presented for the first time The spin-RAM is programmed by magnetization reversal through an interaction of a spin momentum-torque-transferred current and a magnetic moment of memory layers in magnetic tunnel junctions (MTJs), and therefore an external magnetic field is unnecessary as that for a conventional MRAM This new programming mode has been accomplished owing to our tailored MTJ, which has an oval shape of 100 times 150 nm The memory cell is based on a 1-transistor and a 1-MTJ (ITU) structure The 4kbit spin-RAM was fabricated on a 4 level metal, 018 mum CMOS process In this work, writing speed as high as 2 ns, and a write current as low as 200 muA were successfully demonstrated It has been proved that spin-RAM possesses outstanding characteristics such as high speed, low power and high scalability for the next generation universal memory

The emergence of spin electronics in data storage

Abstract: Electrons have a charge and a spin, but until recently these were considered separately. In classical electronics, charges are moved by electric fields to transmit information and are stored in a capacitor to save it. In magnetic recording, magnetic fields have been used to read or write the information stored on the magnetization, which 'measures' the local orientation of spins in ferromagnets. The picture started to change in 1988, when the discovery of giant magnetoresistance opened the way to efficient control of charge transport through magnetization. The recent expansion of hard-disk recording owes much to this development. We are starting to see a new paradigm where magnetization dynamics and charge currents act on each other in nanostructured artificial materials. Ultimately, 'spin currents' could even replace charge currents for the transfer and treatment of information, allowing faster, low-energy operations: spin electronics is on its way.

NVSim: A Circuit-Level Performance, Energy, and Area Model for Emerging Nonvolatile Memory

Abstract: Various new nonvolatile memory (NVM) technologies have emerged recently. Among all the investigated new NVM candidate technologies, spin-torque-transfer memory (STT-RAM, or MRAM), phase-change random-access memory (PCRAM), and resistive random-access memory (ReRAM) are regarded as the most promising candidates. As the ultimate goal of this NVM research is to deploy them into multiple levels in the memory hierarchy, it is necessary to explore the wide NVM design space and find the proper implementation at different memory hierarchy levels from highly latency-optimized caches to highly density- optimized secondary storage. While abundant tools are available as SRAM/DRAM design assistants, similar tools for NVM designs are currently missing. Thus, in this paper, we develop NVSim, a circuit-level model for NVM performance, energy, and area estimation, which supports various NVM technologies, including STT-RAM, PCRAM, ReRAM, and legacy NAND Flash. NVSim is successfully validated against industrial NVM prototypes, and it is expected to help boost architecture-level NVM-related studies.

Phase change memory technology

Abstract: The authors survey the current state of phase change memory (PCM), a nonvolatile solid-state memory technology built around the large electrical contrast between the highly resistive amorphous and highly conductive crystalline states in so-called phase change materials. PCM technology has made rapid progress in a short time, having passed older technologies in terms of both sophisticated demonstrations of scaling to small device dimensions, as well as integrated large-array demonstrators with impressive retention, endurance, performance, and yield characteristics. They introduce the physics behind PCM technology, assess how its characteristics match up with various potential applications across the memory-storage hierarchy, and discuss its strengths including scalability and rapid switching speed. Challenges for the technology are addressed, including the design of PCM cells for low reset current, the need to control device-to-device variability, and undesirable changes in the phase change material that c...

Towards spike-based machine intelligence with neuromorphic computing.

Abstract: Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign. The authors review the advantages and future prospects of neuromorphic computing, a multidisciplinary engineering concept for energy-efficient artificial intelligence with brain-inspired functionality.

Magnetization vector manipulation by electric fields

Abstract: Conventional semiconductor devices use electric fields to control conductivity, a scalar quantity, for information processing. In magnetic materials, the direction of magnetization, a vector quantity, is of fundamental importance. In magnetic data storage, magnetization is manipulated with a current-generated magnetic field (Oersted-Ampere field), and spin current is being studied for use in non-volatile magnetic memories. To make control of magnetization fully compatible with semiconductor devices, it is highly desirable to control magnetization using electric fields. Conventionally, this is achieved by means of magnetostriction produced by mechanically generated strain through the use of piezoelectricity. Multiferroics have been widely studied in an alternative approach where ferroelectricity is combined with ferromagnetism. Magnetic-field control of electric polarization has been reported in these multiferroics using the magnetoelectric effect, but the inverse effect-direct electrical control of magnetization-has not so far been observed. Here we show that the manipulation of magnetization can be achieved solely by electric fields in a ferromagnetic semiconductor, (Ga,Mn)As. The magnetic anisotropy, which determines the magnetization direction, depends on the charge carrier (hole) concentration in (Ga,Mn)As. By applying an electric field using a metal-insulator-semiconductor structure, the hole concentration and, thereby, the magnetic anisotropy can be controlled, allowing manipulation of the magnetization direction.