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Posts Tagged ‘data’

Photonics in Silicon R&D Toward Tb/s

Tuesday, January 3rd, 2017

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By Ed Korczynski, Sr. Technical Editor

The client:server computing paradigm colloquially referred to as the “Cloud” results in a need for extremely efficient Cloud server hardware, and from first principles the world can save a lot of energy resources if servers run on photonics instead of electronics. Though the potential for cost-savings is well known, the challenge of developing cost-effective integrated photonics solutions remains. Today, discrete compound-semiconductor chips function as transmitters, multiplexers (MUX), and receivers of photons, while many global organizations pursue the vision of lower-cost integrated silicon (Si) photonics circuits.

Work on photonics chips—using light as logic elements in an integrated circuit—built in silicon (Si) has accelerated recently with announcements of new collaborative research and development (R&D) projects. Leti, an institute of CEA Tech, announced the launch of a European Commission Horizon 2020 “COSMICC” project to enable mass commercialization of Si-photonics-based transceivers to meet future data-transmission requirements in data centers and super computing systems.

The Leti-coordinated COSMICC project will combine CMOS electronics and Si-photonics with innovative fiber-attachment techniques to achieve 1 Tb/s data rates. These scalable solutions will provide performance improvement an order of magnitude better than current VCSELs transceivers, and the COSMICC-developed technology will address future data-transmission needs with a target cost per bit that traditional wavelength-division multiplexing (WDM) transceivers cannot meet. The project’s 11 partners from five countries are focusing on developing mid-board optical transceivers with data rates up to 2.4 Tb/s with 200 Gb/s per fiber using 12 fibers. The devices will consume less than 2 pJ/bit. and cost approximately 0.2 Euros/Gb/s.

Figure 1: Schematic of COSMICC on-board optical transceiver at 2.4 Tb/s using 50 Gbps/wavelength, 4 CWDM wavelengths per fiber, 12 fibers for transmission and 12 fibers for reception. (Source: Leti)

A first improvement will be the introduction of a silicon-nitride (SiN) layer that will allow development of temperature-insensitive MUX/DEMUX devices for coarse WDM operation, and will serve as an intermediate wave-guiding layer for optical input/output. The partners will also evaluate capacitive modulators, slow-wave depletion modulators with 1D periodicity, and more advanced approaches. These include GeSi electro-absorption modulators with tunable Si composition and photonic crystal electro-refraction modulators to make micrometer-scale devices. In addition, a hybrid III-V on Si laser will be integrated in the SOI/SiN platform in the more advanced transmitter circuits.

Meanwhile in the United States, Coventor, Inc. is collaborating with the Massachusetts Institute of Technology (MIT) on photonics modeling. MIT is a key player in the AIM Photonics program, a federally funded, public-private partnership established to advance domestic capabilities in integrated photonic technology and strengthen high-tech U.S.-based manufacturing. Coventor will provide its SEMulator3D process modeling platform to model the effect of process variation in the development of photonic integrated components.

“Coventor’s technical expertise in predicting the manufacturability of advanced technologies is outstanding. Our joint collaboration with Coventor will help us develop new design methods for achieving high yield and high performance in integrated photonic applications,” said Professor Duane Boning of MIT. Boning is an expert at modeling non-linear effects in processing, many years after working on the semiconductor industry’s reference model for the control of chemical-mechanical planarization (CMP) processing.

—E.K.

Memristor Variants and Models from Knowm

Friday, January 22nd, 2016

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By Ed Korczynski, Sr. Technical Editor

Knowm Inc. (www.knowm.org), a start-up pioneering next-generation advanced computing architectures and technology, recently announced the availability of two new variations of memristors targeting different neuromorphic applications. The company also announced raw device data available for purchase to help researchers develop and improve memristor models. These new Knowm offerings enable the next step in the R&D of radically new chips for pattern-recognition, machine-learning, and artificial intelligence (AI) in general.

There is general consensus between industry and academia and government that future improvements in computing are now severely limited by the amount of energy it takes to use Von Neumann architectures. Consequently, the US Whitehouse has issued a grand challenge with the Energy-Efficient Computing: from Devices to Architectures (E2CDA) program (http://www.nsf.gov/pubs/2016/nsf16526/nsf16526.htm) actively soliciting proposals through March 28, 2016.

The Figure shows a schematic cross-section of Knowm’s memristor devices—with Tin (Sn) and Chromium (Cr) metal layers as the new options to tungsten (W)—along with the device I/V curves for each. “They differ in their activation threshold,” explained Knowm CEO and co-founder Alex Nugent in an exclusive interview with Solid State Technology. “As the activation thresholds become smaller you get reduced data retention, but higher cycle endurance. As that threshold increases you have to dissipate more energy per event, and the more energy you dissipate the faster it will burn-out.” Knowm’s two new memristors, as well as the company’s previously announced device, are now available as unpackaged raw dice with masks designed for research probe stations.

Figure: Schematic cross-section of Knowm’s memristor devices using Tin (Sn) or Chromium (Cr) or tungsten (W) metal layers, along with the device I/V curves for each. (Source: Knowm)

Knowm is working on the simultaneous co-optimization of the entire “stack” from memristors to circuit architectures to application-specific algorithms. “The potential of memristors is so huge that we are seeing exponential growth in the literature, a sort of gold rush as engineers race to design new circuits and re-envision old circuits,” commented Knowm CEO and co-founder Alex Nugent. “The problem is that in the race to publish, circuit designers are adopting models that do not adequately describe real devices.” Knowm’s raw data includes AC, DC, pulse response, and retention for different memristors.

Additional memristors are being developed by Knowm’s R&D lab partner Dr. Kris Campbell of  Boise State University (http://coen.boisestate.edu/kriscampbell/), using different metal layers to achieve different activation thresholds beyond the three shown to date. “She has discovered an algorithm for creating memristors along this dimension,” said Nugent. “From a physics perspective it makes sense that there would be devices with high cycle endurance but reduced data retention.”

“In the future what I image is a single chip with multiple memristors on it. Some will be volatile and very fast, while others will be slow,” continued Nugent. “Just like analog design today uses different capacitors, future neuromophic chips would likely use memristors optimized for different changes in adaptation threshhold. If you think about memristors as fundamental elements—as per Leon Chua (https://en.wikipedia.org/wiki/Leon_O._Chua)—then it makes sense that we’ll need different memristors.”

The applications spaces for these devices have intrinsically different requirements for speed and retention. For example, to exploit these devices for pattern recognition and/or anomaly detection (keeping track of confidence in making temporal predictions) it seems best to choose relatively high activation thresholds because the number of operations is unlikely to burn-out devices. Conversely, for circuits that constantly solve optimization problems the best memristors would require low burn-out and thus low activation thresholds. However, analog applications are generally problematic because the existing memristors leak current, such that stored values degrade over time.

Knowm is shipping devices today, mostly to university researchers, and has tested thousands of devices itself. The Knowm memristors can be fabricated at <500°C using industry-standard unit-process steps, allowing for eventual integration with silicon CMOS “back-end” metallization layers. While still in early R&D, this technology could provide much of the foundation for post-Moore’s-Law silicon ICs.

—E.K.