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2219 search results found
  • Jonathan
    A
    Parker

    Robert C Merton (1970) Professor of Financial Economics
    Primary DLC
    MIT Sloan School of Management

    Contact

    MIT Room
    E62-620
    Phone
    (617) 253-7218
    japarker@mit.edu

    Assistant

    Assistant Name
    Laura Quintiliani
    Assistant phone number
    (617) 253-5858
    lauraq@mit.edu
  • SMR-Logo
    March 7, 2016

    What High-Potential Young Managers Want

  • Conference-AI-Data

    2018 MIT Startup Ecosystem Conference

    Tue, May 15, 2018 Conference
  • 2019 Startup Ecosystem

    2019 MIT Startup Ecosystem Conference

    Thu, May 30, 2019 Conference
    MIT Campus
  • Aragao

    Jeehwan Kim - 2017 ICT Conference

    April 12, 2017Conference Video Duration: 41:34

    Extremely cost-effective semiconductor layer-transfer process via graphene & Highly uniform advanced RRAM

    As a strategy to save the cost of expensive substrates in semiconductor processing, the technique called “layer-transfer” has been developed. In order to achieve real cost-reduction via the “layer-transfer”, the following needs to be insured: (1) Reusability of the expensive substrate, (2) Minimal substrate refurbishment step after the layer release, (3) Fast release rate, and (4) Precise control of a released interface. Although a number of layer transfer methods have been developed including chemical lift-off, optical lift-off, and mechanical lift-off, none of those three methods fully satisfies conditions listed above. In this talk, we will discuss our recent development in a “graphene-based layer-transfer” process that could fully satisfy the above requirements, where epitaxial graphene can serve as a universal seed layer to grow single-crystalline GaN, III-V, II-VI and IV semiconductor films and a release layer that allows precise and repeatable release at the graphene surface. We will further discuss about cost-effective, defect-free heterointergration of semiconductors using graphene-based layer transfers.

    Lastly, I will introduce our new research activities in developing advanced RRAM devices. Resistive switching devices have attracted tremendous attention due to their high endurance, sub-nanosecond switching, long retention, scalability, low power consumption, and CMOS compatibility. RRAMs have also emerged as a promising candidate for non-Von Neumann computing architectures based on neuromorphic and machine learning systems to deal with “big data” problems such as pattern recognition from large amounts of data sets. However, currently reported RRAM devices have not shown uniform switching behaviors across the devices with high on-off ratio which holds up commercialization of RRAM-based data storages as well as demonstration of large-scale neuromorphic functions. Recently, we redesigned RRAM devices and this new device structure exhibits most of functions required for large-array memories and neuromorphic computing, which are (1) excellent retention with high endurance, (2) excellent device uniformity, (3) high on/off current ratio, and (4) current suppression in low voltage regime. I will discuss about the characterization results of this new RRAM device.

    2017 MIT Information and Communication Technologies Conference
  • John Hansman

    Jeehwan Kim - 2017 Japan

    January 27, 2017Conference Video Duration: 35:41

    Extremely cost-effective semiconductor layer-transfer process via graphene & Highly uniform advanced RRAM

    As a strategy to save the cost of expensive substrates in semiconductor processing, the technique called “layer-transfer” has been developed. In order to achieve real cost-reduction via the “layer-transfer”, the following needs to be insured: (1) Reusability of the expensive substrate, (2) Minimal substrate refurbishment step after the layer release, (3) Fast release rate, and (4) Precise control of a released interface. Although a number of layer transfer methods have been developed including chemical lift-off, optical lift-off, and mechanical lift-off, none of those three methods fully satisfies conditions listed above. In this talk, we will discuss our recent development in a “graphene-based layer-transfer” process that could fully satisfy the above requirements, where epitaxial graphene can serve as a universal seed layer to grow single-crystalline GaN, III-V, II-VI and IV semiconductor films and a release layer that allows precise and repeatable release at the graphene surface. We will further discuss about cost-effective, defect-free heterointergration of semiconductors using graphene-based layer transfers.

    Lastly, I will introduce our new research activities in developing advanced RRAM devices. Resistive switching devices have attracted tremendous attention due to their high endurance, sub-nanosecond switching, long retention, scalability, low power consumption, and CMOS compatibility. RRAMs have also emerged as a promising candidate for non-Von Neumann computing architectures based on neuromorphic and machine learning systems to deal with “big data” problems such as pattern recognition from large amounts of data sets. However, currently reported RRAM devices have not shown uniform switching behaviors across the devices with high on-off ratio which holds up commercialization of RRAM-based data storages as well as demonstration of large-scale neuromorphic functions. Recently, we redesigned RRAM devices and this new device structure exhibits most of functions required for large-array memories and neuromorphic computing, which are (1) excellent retention with high endurance, (2) excellent device uniformity, (3) high on/off current ratio, and (4) current suppression in low voltage regime. I will discuss about the characterization results of this new RRAM device.

  • August 1, 2009

    Creating a Better Environment for Finance

  • October 1, 2008

    Business Created from the Innovation Jam

  • 2025 MIT AI Conference

    Tue, April 1, 2025 Conference
    Boston Marriott Cambridge

    For over 80 years, the digital revolution has redefined how we work, learn, and collaborate, reshaping societies and economies worldwide. Today, the rapid advancements in Artificial Intelligence and Machine Learning are accelerating this transformation, pushing the boundaries of what humans and machines can achieve together. 

    The 2025 MIT AI Conference will analyze the latest AI trends, groundbreaking developments, and their profound implications for the future of knowledge, work, skills, and intelligence.

  • SMR-Logo
    April 13, 2020

    Education, disrupted

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