Conrad D. James

A physical model of switching dynamics in tantalum oxide memristive devices

We present resistive switching model for TaOx memristors, which demonstrates that the radius of a tantalum rich conducting filament is the state variable controlling resistance. The model tracks the flux of individual oxygen ions and permits the derivation and solving of dynamical and static state equations. Model predictions for ON/OFF switching were tested experimentally with TaOx devices and shown to be in close quantitative agreement, including the experimentally observed transition from linear to non-linear conduction between RON and ROFF. This work presents a quantitative model of state variable dynamics in TaOx memristors, with direct comparison to high-speed resistive switching data.

Combined field-induced dielectrophoresis and phase separation for manipulating particles in microfluidics

Experiments were conducted in microfluidics equipped with dielectrophoretic gates arranged perpendicular to the flow. Under the action of a high-gradient ac field and shear, flowing suspensions were found to undergo a phase separation and to form a distinct front between the regions enriched with and depleted of particles. We demonstrate that this many-body phenomenon, which originates from interparticle electrical interactions, provides a method for concentrating particles in focused regions and for separating biological and nonbiological materials. The evolution of the particle patterns formation is well described by a proposed electrohydrodynamic model.

Isothermal Switching and Detailed Filament Evolution in Memristive Systems

The steady-state solution of filamentary memristive switching may be derived directly from the heat equation, modelling vertical and radial heat flow. This solution is shown to provide a continuous and accurate description of the evolution of the filament radius, composition, heat flow, and temperature during switching, and is shown to apply to a large range of switching materials and experimental time-scales.

Combined chemical and topographical guidance cues for directing cytoarchitectural polarization in primary neurons.

Chemical and topographical cues can be used to guide dissociated neurons into user-defined network geometries on artificial substrates, yet control of neuron polarity (differentiation into axons and dendrites) remains an elusive goal. We developed a dual guidance cue strategy for directing morphological maturity in neurons in vitro using combined chemical and topographical guidance cues on glass substrates. The surface chemistry provides chemical attraction and repulsion for controlling neuron placement and outgrowth, while the topography provides additional surface area for neuron attachment. Poly-l-lysine (PLL) was adsorbed into etched trenches in glass substrates, and an acetone liftoff process was used to produce bifunctional surfaces with a hydrophobic hexamethyldisilazane (HMDS) background and trench patterns of PLL. We examined the cytoarchitectural polarization of dissociated hippocampal pyramidal neurons on guidance cues designed to promote rapid outgrowth of neurites onto continuous line features and delayed neurite outgrowth onto interrupted line features. An optimum distance of approximately 5 μm between the cell body attachment node and the first interrupted line guidance cue led to specific cytoarchitectural polarization of ≥60% of neurons by 3 days of culture in vitro.

Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding

The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

Monolithic surface micromachined fluidic devices for dielectrophoretic preconcentration and routing of particles

We describe a batch fabrication process for producing encapsulated monolithic microfluidic structures. The process relies on sacrificial layers of silicon oxide to produce surface micromachined fluid channels. Bulk micromachined interconnects provide an interface between the microchannels and meso-scale fluidics. The full integration of the fabrication processing significantly increases device reproducibility and reduces long-term costs. The design and fabrication of dielectrophoresis (DEP) gating structures configured in both batch-flow and continuous-flow modes are detailed. Highly efficient microparticle preconcentration (up to ~100× in 100 s) and valving (97% particle routing efficiency) are demonstrated using ac DEP and an accompanying phase separation. The low aspect-ratio fluid channels with integrated microelectrodes are well suited for µm and sub-µm particle manipulation with electric fields.

High-efficiency magnetic particle focusing using dielectrophoresis and magnetophoresis in a microfluidic device

We describe a novel technique that utilizes simultaneous implementation of dielectrophoresis (DEP) and magnetophoresis (MAP) to focus magnetic particles into streams for optical analysis of biological samples. This technique does not require sheath flow and utilizes a novel interdigitated electrode array chip that yields multiple streams of flowing magnetic particles in single-file columns. The MAP force placed particles in close proximity to the microelectrodes where they were subjected to a strong DEP force that generated the particle focusing effect. Particle focusing efficiency was improved using this combination DEP–MAP technique compared to DEP alone: particle stream widths were reduced ~47% and stream width variability was reduced 80% for focused streams of 8.5 µm diameter magnetic particles. 3 µm diameter magnetic particles were strongly focused with DEP–MAP (~4 µm wide streams with sub-µm variability in stream width) while DEP alone provided minimal focusing. Additional components of a prototype detection system were also demonstrated including an integrated magnetic pelleting component, a hand-held MHz frequency signal generator and a bench-top near-confocal microscope for optical analysis of flowing particles. Preliminary testing of a sandwich assay performed on the surface of magnetic particles showed 50 ppb detection levels of a surrogate biotoxin (ovalbumin) in a raw milk sample.

Continuous-mode dielectrophoretic gating for highly efficient separation of analytes in surface micromachined microfluidic devices

Here, we describe a dielectrophoretic (DEP) gating technique for preconcentrating and separating biological and non-biological particles in a microfluidic device. The microfluidic devices are surface-micromachined on silicon substrates and are fully encapsulated without substrate bonding procedures. DEP gates in the devices consist of embedded microelectrodes that are coupled to the fluid channels for analyte manipulation with electric fields. We consider several different microelectrode designs such as low and high radius-of-curvature edges, and we detail the time- and frequency-dependent preconcentration of particles. Simulations of the particle motion under the manipulation of DEP forces are found to be in good agreement with the experimental results. Experimental results show that bioparticles such as Penicillium brevicompactum (PBC), T-cells and Escherichia coli (E. coli) undergo positive DEP and are trapped in regions of large electric-field gradient adjacent to the DEP gate. In contrast to our previous demonstration of batch-mode separation of particles with different dielectric properties, here we perform a continuous-mode separation of latex particles and E. coli from a mixture.

Resistive memory device requirements for a neural algorithm accelerator.

Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.

Degenerate resistive switching and ultrahigh density storage in resistive memory

We show that in tantalum oxide resistive memories, activation power provides a multi-level variable for information storage that can be set and read separately from the resistance. These two state variables (resistance and activation power) can be precisely controlled in two steps: (1) the possible activation power states are selected by partially reducing resistance, then (2) a subsequent partial increase in resistance specifies the resistance state and the final activation power state. We show that these states can be precisely written and read electrically, making this approach potentially amenable for ultra-high density memories. We provide a theoretical explanation for information storage and retrieval from activation power and experimentally demonstrate information storage in a third dimension related to the change in activation power with resistance.