Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices

While there is an abundance of research on neural networks that are “inspired” by the brain, few mimic the critical temporal compute features that allow the brain to efficiently perform complex computations. Even fewer methods emulate the heterogeneity of learning produced by biological neurons. Memory devices, such as memristors, are also investigated for their potential to implement neuronal functions in electronic hardware. However, memristors in computing architectures typically operate as non-volatile memories, either as storage or as the weights in a multiply-and-accumulate function that requires direct access to manipulate memristance via a costly learning algorithm. Hence, the integration of memristors into architectures as time-dependent computational units is studied, starting with the development of a compact and versatile mathematical model that is capable of emulating flux-linkage controlled analog (FLCA) memristors and their unique temporal characteristics. The proposed model, which is validated against experimental FLCA LixNbO2 intercalation devices, is used to create memristive circuits that mimic neuronal behavior such as desensitization, paired-pulse facilitation, and spike-timing-dependent plasticity. The model is used to demonstrate building blocks of biomimetic learning via dynamical memristive circuits that implement biomimetic learning rules in a self-training neural network, with dynamical memristive weights that are capable of associative lifelong learning. Successful training of the dynamical memristive neural network to perform image classification of handwritten digits is shown, including lifelong learning by having the dynamical memristive network relearn different characters in succession. An analog computing architecture that learns to associate input-to-input correlations is also introduced, with examples demonstrating image classification and pattern recognition without convolution. The biomimetic functions shown in this paper result from fully ion-driven memristive circuits devoid of integrating capacitors and thus are instructive for exploiting the immense potential of memristive technology for neuromorphic computation in hardware and allowing a common architecture to be applied to a wide range of learning rules, including STDP, magnitude, frequency, and pulse shape among others, to enable an inorganic implementation of the complex heterogeneity of biological neural systems

Reproducible Increased Mg Incorporation and Large Hole Concentration in GaN Using Metal Modulated Epitaxy

The metal modulated epitaxy (MME) growth technique is reported as a reliable approach to obtain reproducible large hole concentrations in Mg-doped GaN grown by plasma-assisted molecular-beam epitaxy on c-plane sapphire substrates. An extremely Ga-rich flux was used, and modulated with the Mg source according to the MME growth technique. The shutter modulation approach of the MME technique allows optimal Mg surface coverage to build between MME cycles and Mg to incorporate at efficient levels in GaN films. The maximum sustained concentration of Mg obtained in GaN films using the MME technique was above 7 × 1020 cm-3, leading to a hole concentration as high as 4.5 × 1018 cm-3 at room temperature, with a mobility of 1.1 cm2 V-1 s-1 and a resistivity of 1.3 Ω cm. At 580 K, the corresponding values were 2.6 × 1019 cm-3, 1.2 cm2 V-1 s-1, and 0.21 Ω cm, respectively. Even under strong white light, the sample remained p-type with little change in the electrical parameters. © 2008 American Institute of Physics

Metal Modulation Epitaxy Growth for Extremely High Hole Concentrations Above 10(19) cm(-3) in GaN

The free hole carriers in GaN have been limited to concentrations in the low 1018 cm−3 range due to the deep activation energy, lower solubility, and compensation from defects, therefore, limiting doping efficiency to about 1%. Herein, we report an enhanced doping efficiency up to ~10% in GaN by a periodic doping, metal modulation epitaxy growth technique. The hole concentrations grown by periodically modulating Ga atoms and Mg dopants were over ~1.5 x 1019 cm−3. © 2008 American Institute of Physics

Millimeter Wave Thin-Film Bulk Acoustic Resonator in Sputtered Scandium Aluminum Nitride

This work reports a millimeter wave (mmWave) thin-film bulk acoustic resonator (FBAR) in sputtered scandium aluminum nitride (ScAlN). This paper identifies challenges of frequency scaling sputtered ScAlN into mmWave and proposes a stack and new fabrication procedure with a sputtered Sc0.3Al0.7N on Al on Si carrier wafer. The resonator achieves electromechanical coupling (k2) of 7.0% and quality factor (Q) of 62 for the first-order symmetric (S1) mode at 21.4 GHz, along with k2 of 4.0% and Q of 19 for the third-order symmetric (S3) mode at 55.4 GHz, showing higher figures of merit (FoM, k2xQ) than reported AlN/ScAlN-based mmWave acoustic resonators. The ScAlN quality is identified by transmission electron microscopy (TEM) and X-ray diffraction (XRD), identifying the bottlenecks in the existing piezoelectric-metal stack. Further improvement of ScAlN/AlN-based mmWave acoustic resonators calls for better crystalline quality from improved thin-film deposition methods.Comment: 3 pages, 7 figures, submitted to JMEM

Quantum-statistical transport phenomena in memristive computing architectures

The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has prevented widespread adoption. Here we show coherent electron wave functions play a pivotal role in the nanoscale transport properties of these emerging, non-volatile memories. By characterizing both filamentary and non-filamentary memristive devices as disordered Anderson systems, the switching characteristics and intrinsic variability arise directly from the universality of electron transport in disordered media. Our framework suggests localization phenomena in nanoscale, solid-state memristive systems are directly linked to circuit level performance. We discuss how quantum conductance fluctuations in the active layer set a lower bound on device variability. This finding implies there is a fundamental quantum limit on the reliability of memristive devices, and electron coherence will play a decisive role in surpassing or maintaining Moore's Law with these systems.Comment: 13 pages, 6 figure