Stephen W. Teitsworth
Associate Professor of Physics

Prof. Stephen W. Teitsworth's research centers on experimental, computational, and theoretical studies of deterministic and stochastic nonlinear electronic transport in nanoscale systems. Three particular areas of current interest are: 1) stochastic nonlinear electronic transport phenomena in semiconductor superlattices and tunnel diode arrays; 2) complex bifurcations associated with the deterministic dynamics of electronic transport in negative differential resistance systems; and 3) strategies for stabilizing negative differential resistance systems against the formation of space-charge waves.

Current Research Interests

  • Current research centers on experimental, theoretical and computational investigation of rare fluctuation processes in noise-driven nonlinear dynamical systems that are far from thermal equilibrium.  The experimental platforms for studying these phenomena are bistable electronic transport systems, principally semiconductor superlattices and tunnel diodes.  Theoretical work utilizes stochastic Lagrangian methods, while computational work focuses on the direct simulation of systems of nonlinear stochastic differential equations.  Specific projects include:


  1. Experimental of novel scaling behavior in noise-driven tunnel diodes:  Over the past five years, I have worked with Duke undergrads to carry out precision measurements of noise-induced switching between co-existing metastable states in a tunnel diode circuit.  We studied how the mean switching time scales with applied voltage and noise intensity.  We were able to measure switching times over an exceptionally wide range, by far the largest dynamic range ever reported for this type of measurement.  These measurements are particularly challenging because the scaling law depends exponentially on control parameter, typically an applied voltage, thus requiring exquisitely precise voltage control.  In the first round of measurements, we successfully verified a long-predicted scaling law for switching times where the switching dynamics is essentially one-dimensional.  This work has been published in The European Physical Journal B in 2019.  Future plans include studying these switching effects in arrays of tunnel diodes where the possibility of multiple competing transition pathways emerges.  Additionally, an NSF proposal is planned for submission which would permit extension of these investigations to include related measurements in semiconductor superlattices as well as novel memristive materials.  Such measurements require more elaborate student training and expense since they must be performed at cryogenic temperatures and samples much be processed and custom-grown utilizing sophisticated equipment, some of which is found in the Shared Materials Instrumentation Facility at Duke.  A technically interesting motivation for these measurements lies in the possibility to use noise-enhanced dynamics in superlattice electronic transport as the basis for a new class of ultra-compact and high bandwidth physical random number generators.


2. Theory and simulation of noise-driven nonlinear dynamical systems:  Throughout 2019, I have continued a fruitful collaboration with Prof. John Neu (UC Berkeley, Math) to develop a quantitative theory of rare fluctuation processes in nonlinear dynamical systems.  In particular, we have utilized a geometric Lagrangian approach and topological methods to study conditions whereby the most likely fluctuation pathway should exhibit bifurcation behavior.   This first portion of this work has been published as a chapter in the conference proceedings entitled “Coupled Mathematical Models for Physical and Nanoscale Systems and their Applications,” published by Springer.  We are currently completing a manuscript titled "Stochastic Line Integrals as Metrics for Irreversibility."


  1. Theory and experimental measurement of fluctuation loops in linear noise-driven dynamical systems: Noise-driven linear dynamical systems appear throughout the natural sciences, for example, as a useful modelling technique for describing the statistical variations in surface ocean temperatures associated with the El Niño climate pattern.  In the case of climate modelling, an alternative approach centers around the use of high-dimensional global circulation models which are inherently limited by available computing power.  Linear stochastic systems show promise to achieve similar predictabilities but with far fewer computational resources.  In 2018 and 2019, our accomplishments included: 1) performing experimental measurements which confirm theoretical predictions, and 2) extending a previously defined effective area tensor to nonlinear systems.  The experimental work has been in Physical Review in 2019 with title “Experimental metrics for detection of detailed balance violation.”  The theoretical work is currently in preparation with a draft title “Characterizing the breaking of detailed balance in noise-driven nonlinear systems.”


4.  Exploratory research on noise-driven switching in memristor devices:  In Spring 2019 and working with a Duke undergrad, I carried out exploratory research on theory and modeling of electronic current switching dynamics in memristor materials.  Memristors have attracted considerable attention during the past few years for their potential application as a new physical substrate for achieving highly dense, low power memory and logic devices, as well as new modalities such as neuromorphic computation.  A common feature to all memristor materials is that they exhibit bistability in their electrical resistance.  As one goes to smaller length and energy scales, noise effects can be expected to play an increasingly prominent role.  We are utilizing the experimental and theoretical methods that I have already developed for superlattices and tunnel diodes to study memristive materials with an eye to achieving new insights into the fundamental performance limits of memristors as switching devices.  

Current Appointments & Affiliations

Contact Information

  • 089 Physics Bldg, Durham, NC 27708
  • Box 90305, Durham, NC 27708-0305

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