Home
Our group's broad goal is to develop novel, useful ways to perform computations. We pursue highly interdisciplinary research, which spans material science, electrical engineering, and computer science. It involves both theoretical and experimental work, including developing new functional materials, modeling and prototyping novel electronic devices and circuit architectures, and designing relevant algorithms. The current focus is on mixed-signal circuit implementations with emerging memory devices for applications in machine learning, artificial intelligence, combinatorial optimization, and hardware security.
Open Positions
Outstanding candidates are invited to apply for an immediate postdoc opening in the area of combinatorial optimization and neuromorphic computing. The position best suits an electrical engineer with a strong mixed-signal / analog circuit design background and design tapeout experience. The position is for one year with the possibility of renewal for up to three years. Graduate and undergraduate students will also be considered and encouraged to apply. Interested candidates should send CVs to Prof. Strukov.
Representative Publications
Device technology
H. Kim et al. 4K-memristor analog-grade passive crossbar circuit. Nat. Commun. 12, 5198 (2021)
G.C. Adam et al. 3-D memristor crossbars for analog and neuromorphic computing applications. IEEE TED 64, 312 (2017)
F. Merrikh Bayat et al. Redesigning commercial floating-gate memory for analog computing applications. ISCAS'15, 1921 (2015)
Device modeling
D.B. Strukov et al. Thermophoresis/diffusion as the plausible mechanism for unipolar resistive switching in metal-oxide-metal memristors. Appl. Phys. A 107, 509 (2012)
D.B. Strukov et al. Exponential ionic drift: Fast switching and low volatility of thin film memristors. Appl. Phys. A 94, 515 (2009)
D.B. Strukov et al. Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior. Small 5,1058 (2009)
Circuit architectures
M. Bavandpour et al. aCortex: An energy-efficient multi-purpose mixed-signal inference accelerator. IEEE JXCDC 6, 98 (2020)
A. Madhavan et al. Race logic: A hardware acceleration for dynamic programming algorithms. ISCA’14, 517 (2014)
D.B. Strukov et al. Topological framework for three-dimensional circuits with multilayer crossbar arrays. PNAS 106, 20155 (2009)
D.B. Strukov et al. CMOL FPGA: A cell-based, reconfigurable architecture for hybrid digital circuits using two-terminal nanodevices. Nanotechnol. 16, 888 (2005)
Neural network circuits prototyping
M. Mahmoodi et al. Versatile stochastic dot product circuits based on nonvolatile memories for high-performance neurocomputing and neurooptimization. Nat. Commun. 10, 5113 (2019)
F. Merrikh Bayat et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat. Commun. 9, 2331 (2018)
M. Prezioso et al. Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits. Nat. Commun. 9, 5311 (2018)
X. Guo et al. Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded NOR flash memory technology. Proc. IEDM'17, 6.5.1 (2017)
Hardware security
M.R. Mahmoodi et al. A strong physically unclonable function with > 280 CRPs and < 1.4% BER using passive ReRAM technology. IEEE SSC-L 3, 182 (2020)
M. Mahmoodi et al. Ultra-low power physical unclonable function with nonlinear fixed-resistance crossbar circuits. IEDM'19, 30.1.1 (2019)
H. Nili et al. Hardware-intrinsic security primitives enabled by analogue state and nonlinear conductance variations in integrated memristors. Nat. Electron. 1, 197 (2018)
Facilities, Equipment, and Other Resources
Our lab is equipped with electrical characterization tools, including a cryogenic probe station, Agilent B1500 parameter analyzers, and Agilent 81180A arbitrary waveform generator. The cleanroom fabrication facilities (state-of-the-art e-beam, photo and nanoimprinting lithographies, various deposition tools, etc.) and material characterization tools (XPS, SIMS, SEM, TEM, AFM) are managed by the UCSB nanofabrication center and Materials department, respectively. The experimental and theoretical work is aided by COMSOL, Matlab, Labview, Cadence software tools and NVidea Tesla S1070 computing system, and high-performance computing resources at the Center for Scientific Computing
Funding
We gratefully acknowledge our current and past sponsors: AFOSR, Applied Materials Inc, ARO, DARPA, DENSO Corp., Google Inc, Hellman Family Foundation, Hewlett Packard Laboratories, UCSB's Institute of Energy Efficiency, NRL, NSF, ONR, Samsung, and SRC.