9.03.2015

Eureka Moments Lead to Radical New Memory Design

Koelmans and Sebastian in the lab where the
new memory design was developed
IBM researcher Wabe Koelmans had the first significant eureka moment of his young promising career on Saturday, 14 December 2013.

The Dutch scientist is focused on emerging memory technologies based on what are known as phase change materials. Such materials can be used to store data quickly, simply by applying electrical pulses to the material, which is sandwiched between two metal electrodes.

Koelmans started his experiment the prior evening at IBMs Zurich Lab and then went home for the night to monitor the results from the comfort of his apartment. By Saturday he and his colleague Abu Sebastian, were already seeing significant progress.

After Koelmans made a small tweak to the experiment they exchanged some messages and similar to waiting for a movie to finish downloading, they anxiously stared at their screens and waited.

It was extremely addictive to check the experiments progress, so we were both very excited and logging in regularly. I ran over 60 experiments, each taking half an hour, in that weekend, but it was worth it, said Koelmans.

By Sunday morning with a cup of coffee Koelmans saw the remarkable drift was virtually non-existent. The significance of this requires a bit of explaining.

An Introduction to Drift

Phase change memory (PCM) devices have been investigated since the early 1970s and in the past 12 months IBM scientists have made tremendous progress with this technology publishing a number of milestone papers which demonstrate multiple bits per cell, making the technology extremely competitive with Flash.

But PCM doesnt come without some drawbacks, a primary culprit being resistance drift. Drift is the change in resistance of the stored levels over time. Essentially the data moves causing your text document or your photo to eventually become corrupted and unusable a very bad characteristic for a storage technology.
    Fig B: The desired IV characteristics corresponding to the 
phase-change and projection segments are shown schematically
Credit: Nature Communications  

A significant breakthrough to eliminate drift came a couple of years back when IBM scientists from Zurich and Yorktown Heights, NY proposed a novel device concept where the physical mechanism of writing is decoupled from the read process. To use an analogy, during the write cycle the electric pulse goes straight down through the material like water washing over a boulder in the middle of a river. But as in figure B during the read, the pulse goes around the material, or the boulder, decoupling the write and the read. This allows storing information without suffering from the drift that normally comes with it.

Patents for these inventions are currently pending or have been assigned including Phase-change Memory Cells (2015), Resistive Random-Access Memory Cells and Phase-Change Memory Cells (2014), see also IEDM 2013.

Abu Sebastian, one of the IBM co-inventors of the concept comments, Over the years, IBM scientists have developed powerful techniques mostly based on read/write methodology and signal processing to counter resistance drift in order to facilitate multi-bit storage. However, a device-level solution with substantially lower drift will further improve the error-rate performance and could enable the storage of a higher number of bits per device using the same techniques.”


Despite the promise of the concept, a physical realization of such a device with a conclusive experimental demonstration of all its benefits was still missing.

Eureka 2.0

During a period of eight months, Koelmans and his colleagues continued optimizing the design and materials to achieve fast write speeds and high endurance, attributes that make PCM a compelling memory candidate for high-throughput applications in the cloud.

Then came Monday, 30 March 2015. On this fateful spring day, Koelmans had his second significant eureka moment: near elimination of noise.

In PCM devices, besides drift, the resistance levels also experience random fluctuations. This low-frequency noise (1/f) behavior is a nightmare for multi-level storage. Drift having been dealt with, this noise is arguably the second largest challenge that limits the number of bits one could store in a single PCM device. Koelmans observed almost complete elimination of this 1/f noise in these devices.


Its easy to focus on the near-elimination of resistance drift, but this is only one of the benefits. The two others: much reduced 1/f noise and much reduced - and predictable - temperature dependence are also very important. In the end, you need a stable signal (resistance) over time and temperature and a good signal-to-noise ratio to facilitate multi-level PCM, said Koelmans.

Schematic 3D view of projected 
phase-change memory devices with lateral geometry.
Credit: Nature Communications

The next morning Koelmans ran into the office of team leader and IBM Fellow Evangelos Eleftheriou who was astonished, We need to publish this right away.

Five months later Koelmans and the team delivered. Today in the peer-reviewed journal of Nature Communications (10.1038/NCOMMS9181) this new, radical memory cell design is being reported on for the first time which the team calls projected phase change memory, which features virtually no drift and very little noise. The paper introduces the concept, along with the design, fabrication, and simulation of such devices with the results.

IBM scientists believe that projected phase change memory devices could also play a key role in future non-Von Neumann computing paradigms such as brain-inspired neuromorphic computing.

8.24.2015

Helping customers before they call


by Francesco Calabrese, manager of Smarter Urban Dynamics, IBM Research-Ireland 

As our smartphones get smarter, we’re using more Over-The-Top applications like WhatsApp, Viber or Skype, rather than our telco provider’s voice, and text. This downward trend means shrinking income for the telco, even though it’s estimated that data usage will grow beyond 20 exabytes per month in the coming years. When my team of social, mobile and decision theory researchers at IBM’s lab in Dublin noticed this OTT trend, we wanted to know, in broader terms: could we measure and predict the quality of a customer’s experience on a telco network in real time?

8.23.2015

Wickets, tweets, and a 3-D printer


by Josh Andres, User Experience, Design, Human Computer Interaction, IBM Research-Australia

What do 3-D printing, Twitter, and the sport of cricket have in common? Probably not a whole lot to most people.  But IBM intern Rohit Ashok Khot and I have been experimenting with ways to explore the benefits of visualizing personalized sports summaries in a tangible, 3-D form, here in IBM’s research lab in Melbourne. Basing our study on a cricket series between Bangladesh and India – two “heavy hitters” in the cricket world – we combined the power of real-time analytics and social media with the possibilities of 3-D printing.

8.21.2015

IBM's New Polymers Acclaimed for Use in 3-D Printing


by Courtney Fox, Ph.D., Research Scientist, Carbon3D, Inc.

Polymeric materials have become an integral part of our lifestyle over the last century. They're made into an enormous range of products, from simple and inexpensive injection-molded toys and tchotchkes to super-strong bullet-proof vests and high-performance water-separation membranes.

Recent enhancements in both chemical and computational capabilities are giving scientists the ability to create specially designed polymers aimed at solving a host of important materials challenges.

8.17.2015

Tachyon for ultra-fast Big Data processing


Editor’s note: This article is by cloud analytics infrastructure expert Gil Vernik, IBM Research-Haifa.

Today's massive growth in data sets means that storage is increasingly becoming a critical bottleneck for system workloads. My storage team in Haifa, Israel wants to analyze and understand these massive volumes of data, and we need to store them somewhere reliable. Although disk space is an option, it's too slow to carry out fast Big Data processing. In-memory computing, which keeps the data in a server’s RAM for fast access and processing, offers a good solution for processing Big Data workloads but it’s limited and expensive. 

Enter Tachyon, a memory-centric distributed storage system that offers processing at memory-speed and reliable storage. Its software works with servers in clusters so there’s plenty of room for storage, and a unique proprietary feature eliminates the need for replication to ensure fault tolerance. Now, we’ve connected Tachyon to Swift so it can work effortlessly with Swift and SoftLayer. The result? Tachyon is even more flexibile and efficient.

IBM z13 Technology and Design

Special Issue of the IBM Journal of Research and Development

This special issue of the IBM Journal of Research and Development describes the innovations and technology in the IBM z13, the latest mainframe with significant new capabilities, along with enhanced capacity, security, data serving, virtualization, reliability, and robustness. This new system is specifically designed for big data, analytics, mobile transactions, and cloud computing.

8.11.2015

IBM DeepCurrent Predicts Environmental Changes in 3-D

From ocean bays in Ireland to fresh water lakes in New York, IBM “smarter water” scientists have developed a way to forecast the health of bodies of water — in 3-D. IBM DeepCurrent laps up data from coastlines to underwater depths via sensors attached to everything from buoys to tagged fish. It creates 3-D models that can zoom in on a location or event to provide accurate predictions of environmental dynamics, such as salt runoff or invasive species. So, business owners, environmental agencies and public administrators can use it to improve their accuracy in marine monitoring to better manage and forecast for potentially destructive environmental events.
Emanuele Ragnoli
“IBM DeepCurrent is a geophysical fluid dynamics tool that focuses on fluid motion in the earth’s water systems. It simulates the flow of water and corresponding changes in its properties, such as variations in temperature, salinity and nutrient concentrations."


“And it can also tell us about water flows, water quality, and other environmental parameters by using physical models, machine learning and control theory algorithms, combined with sensor data, in a particular coastal area,” said Emanuele Ragnoli, an IBM Environmental Analytics researcher in Dublin, Ireland.