Overview

    Research Overview
The general focus of our research is on the development of novel ways to perform computation. Our research is highly interdisciplinary in nature, spanning across material science, physics, electrical engineering and computer science, and involves both theoretical and experimental work.  This includes development of functional materials, exploration of novel electronic devices and high performance circuit architectures for general purpose and application specific tasks, and development of relevant algorithms and software tools. Our current research focus is on hardware implementations of mixed-signal artificial neural networks with emerging memory devices.

    Selected Recent Papers

       Mixed-signal image classifier based on 100K+ 180-nm embedded  NOR flash cells:
F. Merrikh Bayat, X. Guo, M. Klachko, M. Prezioso, K. K. Likharev, and D. B. Strukov, “High-performance mixed-signal neurocomputing with nanoscale floating-gate memory cells”, IEEE Trans. Neural Networks & Learning Systems 29, pp. 4782-4790, 2018.

       STDP learning of coincidence detection in memristor-based neural network:
M. Prezioso, M. Mahmoodi, F. Merrikh Bayat, H. Kim, H. Nili, A. Vincent, and D. Strukov, "Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits", Nature Communications 9, art. 5311, 2018.

       Mixed-signal multilayer perceptron network based on passively integrated metal-oxide memristors:
F. Merrikh Bayat, M. Prezioso, B. Chakrabarti, H. Nili, I. Kataeva, and D. Strukov, "Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits", Nature Communications 9, art. 2331, 2018.

       Hardware security with memristors:
H. Nili, G.C. Adam, B. Hoskins, M. Prezioso, J. Kim, M.R. Mahmoodi, F. Merrikh Bayat, O. Kavehei, and D.B. Strukov, "Hardware-intrinsic security primitives enabled by analogue state and nonlinear conductance variations in integrated memristors", Nature Electronics 1, pp. 197–202, 2018 (journal cover)

       CMOS-compatible stackable crossbar circuits with passively integrated highly-uniform analog-grade memristors:
G. C. Adam, B. D. Hoskins, M. Prezioso, F. Merrikh-Bayat, B. Chakrabarti, and D.B. Strukov, “3-D memristor crossbars for analog and neuromorphic computing applications”, IEEE Trans. Electron Devices 64 (1), pp. 312-318, 2017.

       3D logic-in-memory computing - a pathway towards solving  Feynman's grand challenge:
G. C. Adam, B. D. Hoskins, M. Prezioso, and D. B. Strukov, “Optimized stateful material implication logic for 3D data manipulation”, Nano Research 9, art. 3914, 2016.

       Experimental demonstration of Hopfield neural network with memristors:
X. Guo, F. Merrikh Bayat, L. Gao, B. D. Hoskins, F. Alibart, B. Linares-Barranco, L. Theogarajan, C. Teuscher, and D.B. Strukov, "Modeling and experimental demonstration of a Hopfield network analog-to-digital converter with hybrid CMOS/memristor circuits", Frontiers in Neuroscience 9, art. 488, Dec. 2015.

    Lab Facilities
Our lab is equipped with various electrical characterization tools including cryogenic probe station, Agilent B1500 parameter analyzers, and Agilent 81180A arbitrary waveform generator. The fabrication facilities (cleanroom with state-of-the-art e-beam, photo and nanoimprinting lithographies, various deposition tools etc.) and material characterization tools (XPS, SIMS, SEM, TEM, AFM) are provided by UCSB nanofabrication center and Materials department. The experimental and theoretical work is aided with COMSOL, Matlab, Labview, Cadence software and NVidea Tesla S1070 computer.

    Funding
We gratefully acknowledge our sponsors: AFOSR, ARO, DARPA, DENSO Corp., Google Inc, Hellman Family Foundation,
Hewlett Packard Laboratories, NSF, Samsung, and SRC.
    Open positions
          There are several openings for PhD students starting Fall 2018 in VLSI, circuit design, and device fabrication areas.            
          Outstanding candidates are also invited for an immediate postdoc position.

Last updated on December 19, 2018