Search

Artificial realizations of the mammalian brain alongside their integration into electronic components are explored through neuromorphic architectures, neuroarchitectectonics, on CMOS compatible platforms. Exploration of neuromorphic technologies continue to develop as an alternative computational paradigm as both capacity and capability reach their fundamental limits with the end of the transistor-driven industrial phenomenon of Moore’s law. Here, we consider the electronic landscape within neuromorphic technologies and the role of the atomic switch as a model device. We report the fabrication of an atomic switch network (ASN) showing critical dynamics and harness criticality to perform benchmark signal classification and Boolean logic tasks. Observed evidence of biomimetic behavior such as synaptic plasticity and fading memory enable the ASN to attain a cognitive capability within the context of artificial neural networks.

Efforts to achieve a low-power, dynamically complex system become crucial as CMOS fabrication limits are realized. Atomic Switch Networks (ASNs) provide fabrication advantages over traditional CMOS through the combination of top-down and bottom-up techniques, leading to densely inter-connected networks of atomic switches. ASNs show emergent behaviors through the interaction of individual non-linear elements. These properties make ASNs suitable for alternative computational paradigms, such as neuromorphic or reservoir computing. This work examined ASNs' ability to perform Boolean logic operations using non-temporal inputs based on randomized Boolean input streams. Zero and one bits were converted to negative and positive DC voltage pulses, respectfully. Next, a linear readout layer was applied to an array of voltage outputs from the device to reconstruct target output signals for the given task. ASNs produced nearly perfect results at low voltages for AND, OR, and NAND with more than 95% confidence. XOR, which requires non-linearity to solve, was able to be partially solved at high voltages with more than 95% confidence. As opposed to previous works which have investigated temporal computation in ASNs, this work was the first to demonstrate semi-predictable, non-temporal, non-linear behavior within the device. Results demonstrated that the device connectivity is complete enough to perform complex computations.

Stem cell-derived cardiomyocytes provide a promising tool for human developmental biology, regenerative therapies, disease modeling, and drug discovery. As human pluripotent stem cell-derived cardiomyocytes remain functionally fetal-type, close monitoring of electrophysiological maturation is critical for their further application to biology and translation. However, to date, electrophysiological analyses of stem cell-derived cardiomyocytes has largely been limited by biologically undefined factors including 3D nature of embryoid body, sera from animals, and the feeder cells isolated from mouse. Large variability in the aforementioned systems leads to uncontrollable and irreproducible results, making conclusive studies difficult. In this report, a chemically-defined differentiation regimen and a monolayer cell culture technique was combined with multielectrode arrays for accurate, real-time, and flexible measurement of electrophysiological parameters in translation-ready human cardiomyocytes. Consistent with their natural counterpart, amplitude and dV/dtmax of field potential progressively increased during the course of maturation. Monolayer culture allowed for the identification of pacemaking cells using the multielectrode array platform and thereby the estimation of conduction velocity, which gradually increased during the differentiation of cardiomyocytes. Thus, the electrophysiological maturation of the human pluripotent stem cell-derived cardiomyocytes in our system recapitulates in vivo development. This system provides a versatile biological tool to analyze human heart development, disease mechanisms, and the efficacy/toxicity of chemicals.

Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime.

We discuss the utility of ASNs as a uniquely scalable physical platform capable of hybrid-CMOS architectures and novel computation. Through a combination of controlled design with spontaneous self-organization, an atomic switch network (ASN) has been produce as a purpose-built complex system. A highly interconnected system of Ag2S resistive switches, the ASN has been shown to produce fault-tolerant switching and a set of complex dynamics similar to biological neural networks.

Luminescent waveguides (LWs) occur in a wide range of applications, from solar concentrators to doped fiber amplifiers. Here we report a comprehensive analysis of escape-cone losses in LWs, which are losses associated with internal rays making an angle less than the critical angle with a waveguide surface. For applications such as luminescent solar concentrators, escape-cone losses often dominate all others. A statistical treatment of escape-cone losses is given accounting for photoselection, photon polarization, and the Fresnel relations, and the model is used to analyze light absorption and propagation in waveguides with isotropic and orientationally aligned luminophores. The results are then compared to experimental measurements performed on a fluorescent dye-doped poly(methyl methacrylate) waveguide.

Developments in computing hardware are constrained by the operating principles of complementary metal oxide semiconductor (CMOS) technology, fabrication limits of nanometer scaled features, and difficulties in effective utilization of high density interconnects. This set of obstacles has promulgated a search for alternative, energy efficient approaches to computing inspired by natural systems including the mammalian brain. Atomic switch network (ASN) devices are a unique platform specifically developed to overcome these current barriers to realize adaptive neuromorphic technology. ASNs are composed of a massively interconnected network of atomic switches with a density of ∼109 units/cm2 and are structurally reminiscent of the neocortex of the brain. ASNs possess both the intrinsic capabilities of individual memristive switches, such as memory capacity and multi-state switching, and the characteristics of large-scale complex systems, such as power-law dynamics and non-linear transformations of input signals. Here we describe the successful nanoarchitectonic fabrication of next-generation ASN devices using combined top-down and bottom-up processing and experimentally demonstrate their utility as reservoir computing hardware. Leveraging their intrinsic dynamics and transformative input/output (I/O) behavior enabled waveform regression of periodic signals in the absence of embedded algorithms, further supporting the potential utility of ASN technology as a platform for unconventional approaches to computing.

Self-organized complex systems are ubiquitous in nature, and the structural complexity of these natural systems can be used as a model to design new classes of functional nanotechnology based on highly interconnected networks of interacting units. Conventional fabrication methods for electronic computing devices are subject to known scaling limits, confining the diversity of possible architectures. This work explores methods of fabricating a self-organized complex device known as an atomic switch network and discusses its potential utility in computing. Through a merger of top-down and bottom-up techniques guided by mathematical and nanoarchitectonic design principles, we have produced functional devices comprising nanoscale elements whose intrinsic nonlinear dynamics and memorization capabilities produce robust patterns of distributed activity and a capacity for nonlinear transformation of input signals when configured in the appropriate network architecture. Their operational characteristics represent a unique potential for hardware implementation of natural computation, specifically in the area of reservoir computing-a burgeoning field that investigates the computational aptitude of complex biologically inspired systems.