Integrating Memristors For Neuromorphic Computing


Much of the current research on neuromorphic computing focuses on the use of non-volatile memory arrays as a compute-in-memory component for artificial neural networks (ANNs). By using Ohm’s Law to apply stored weights to incoming signals, and Kirchoff’s Laws to sum up the results, memristor arrays can accelerate the many multiply-accumulate steps in ANN algorithms. ANNs are being dep... » read more

More Lithography/Mask Challenges


Semiconductor Engineering sat down to discuss lithography and photomask technologies with Gregory McIntyre, director of the Advanced Patterning Department at [getentity id="22217" e_name="Imec"]; Harry Levinson, senior fellow and senior director of technology research at [getentity id="22819" comment="GlobalFoundries"]; Regina Freed, managing director of patterning technology at [getentity id="... » read more

Blog Review: May 2


Arm's Greg Yeric looks towards the future of 3D ICs with a dive into transistor-level 3D, including the different proposed methods of stacking transistors, power/performance benefits, and challenges such as parasitic resistance. Mentor's Kurt Takara, Chris Kwok, Dominic Lucido, and Joe Hupcey III explain how a custom synchronizer methodology can help avoid CDC mistakes and errors in FPGA des... » read more

Xceler Systems: Graph Architecture


An inventor who made foundational contributions to three key ways we move data through complex systems is developing a new type of neuromorphic chip to accelerate AI applications. Rather than try to build a computer that looks like a brain, Gautam Kavipurapu and Xceler Systems are building smaller bits that act like synapses. When the design is advanced enough and there are enough of them, t... » read more

How The Brain Saves Energy By Doing Less


One of the arguments for neuromorphic computing is the efficiency of the human brain relative to conventional computers. By looking at how the brain works, this argument contends, we can design systems that accomplish more with less power. However, as Mireille Conrad and others at the University of Geneva pointed out in work presented at December's IEEE Electron Device Meeting, the brain... » read more

What If We Had Bi-Directional RRAM?


The ideal memristor device for neuromorphic computing would have linear and symmetric resistance behavior. Resistance would both increase and decrease gradually, allowing a direct correlation between the number of programming pulses and the resistance value. Real world RRAM devices, however, generally do not have these characteristics. In filamentary RRAM devices, the RESET operation can raise ... » read more

What’s Next In Neuromorphic Computing


To integrate devices into functioning systems, it's necessary to consider what those systems are actually supposed to do. Regardless of the application, [getkc id="305" kc_name="machine learning"] tasks involve a training phase and an inference phase. In the training phase, the system is presented with a large dataset and learns how to "correctly" analyze it. In supervised learning, the data... » read more

New Nodes, Materials, Memories


Ellie Yieh, vice president and general manager of Advanced Product Technology Development at [getentity id="22817" e_name="Applied Materials"], and head of the company's Maydan Technology Center, sat down with Semiconductor Engineering to talk about challenges, changes and solutions at advanced nodes and with new applications. What follows are excerpts of that conversation. SE: How far can w... » read more

System Bits: Jan. 30


Lab-in-the-cloud Although Internet-connected smart devices have penetrated numerous industries and private homes, the technological phenomenon has left the research lab largely untouched, according to MIT researchers. Spreadsheets, individual software programs, and even pens and paper remain standard tools for recording and sharing data in academic and industry labs — until now. TetraScie... » read more

System Bits: Jan. 23


Artificial synapse for “brain-on-a-chip” portable AI devices In the emerging field of neuromorphic computing, researchers are attempting to design computer chips that work like the human brain, which, instead of carrying out computations based on binary, on/off signaling like digital chips do today, the elements of a brain-on-a-chip would work in an analog fashion, exchanging a gradient of... » read more

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