Diffusing-wave spectroscopy of nonergodic media

We introduce an elegant method which allows the application of diffusing-wave spectroscopy (DWS) to nonergodic, solid-like samples. The method is based on the idea that light transmitted through a sandwich of two turbid cells can be considered ergodic even though only the second cell is ergodic. If absorption and/or leakage of light take place at the interface between the cells, we establish a so-called "multiplication rule", which relates the intensity autocorrelation function of light transmitted through the double-cell sandwich to the autocorrelation functions of individual cells by a simple multiplication. To test the proposed method, we perform a series of DWS experiments using colloidal gels as model nonergodic media. Our experimental data are consistent with the theoretical predictions, allowing quantitative characterization of nonergodic media and demonstrating the validity of the proposed technique.Comment: RevTeX, 12 pages, 6 figures. Accepted for publication in Phys. Rev.

Fast, non-monte-carlo estimation of transient performance variation due to device mismatch

This paper describes an efficient way of simulating the effects of device random mismatch on circuit transient characteristics, such as variations in delay or in frequency. The proposed method models DC random offsets as equivalent AC pseudo-noises and leverages the fast, linear periodically time-varying (LPTV) noise analysis available from RF circuit simulators. Therefore, the method can be considered as an extension to DC match analysis and offers a large speed-up compared to the traditional Monte-Carlo analysis. Although the assumed linear perturbation model is valid only for small variations, it enables easy ways to estimate correlations among variations and identify the most sensitive design parameters to mismatch, all at no additional simulation cost. Three benchmarks measuring the variations in the input offset voltage of a clocked comparator, the delay of a logic path, and the frequency of an oscillator demonstrate the speed improvement of about 100-1000x compared to a 1000-point Monte-Carlo method

Bandgap engineering in semiconductor alloy nanomaterials with widely tunable compositions

Over the past decade, tremendous progress has been achieved in the development of nanoscale semiconductor materials with a wide range of bandgaps by alloying different individual semiconductors. These materials include traditional II-VI and III-V semiconductors and their alloys, inorganic and hybrid perovskites, and the newly emerging 2D materials. One important common feature of these materials is that their nanoscale dimensions result in a large tolerance to lattice mismatches within a monolithic structure of varying composition or between the substrate and target material, which enables us to achieve almost arbitrary control of the variation of the alloy composition. As a result, the bandgaps of these alloys can be widely tuned without the detrimental defects that are often unavoidable in bulk materials, which have a much more limited tolerance to lattice mismatches. This class of nanomaterials could have a far-reaching impact on a wide range of photonic applications, including tunable lasers, solid-state lighting, artificial photosynthesis and new solar cells

Resource-Constrained Adaptive Search and Tracking for Sparse Dynamic Targets

This paper considers the problem of resource-constrained and noise-limited localization and estimation of dynamic targets that are sparsely distributed over a large area. We generalize an existing framework [Bashan et al, 2008] for adaptive allocation of sensing resources to the dynamic case, accounting for time-varying target behavior such as transitions to neighboring cells and varying amplitudes over a potentially long time horizon. The proposed adaptive sensing policy is driven by minimization of a modified version of the previously introduced ARAP objective function, which is a surrogate function for mean squared error within locations containing targets. We provide theoretical upper bounds on the performance of adaptive sensing policies by analyzing solutions with oracle knowledge of target locations, gaining insight into the effect of target motion and amplitude variation as well as sparsity. Exact minimization of the multi-stage objective function is infeasible, but myopic optimization yields a closed-form solution. We propose a simple non-myopic extension, the Dynamic Adaptive Resource Allocation Policy (D-ARAP), that allocates a fraction of resources for exploring all locations rather than solely exploiting the current belief state. Our numerical studies indicate that D-ARAP has the following advantages: (a) it is more robust than the myopic policy to noise, missing data, and model mismatch; (b) it performs comparably to well-known approximate dynamic programming solutions but at significantly lower computational complexity; and (c) it improves greatly upon non-adaptive uniform resource allocation in terms of estimation error and probability of detection.Comment: 49 pages, 1 table, 11 figure

Contributions on using embedded memory circuits as physically unclonable functions considering reliability issues

[eng] Moving towards Internet-of-Things (IoT) era, hardware security becomes a crucial research topic, because of the growing demand of electronic products that are remotely connected through networks. Novel hardware security primitives based on manufacturing process variability are proposed to enhance the security of the IoT systems. As a trusted root that provides physical randomness, a physically unclonable function is an essential base for hardware security. SRAM devices are becoming one of the most promising alternatives for the implementation of embedded physical unclonable functions as the start-up value of each bit-cell depends largely on the variability related with the manufacturing process. Not all bit-cells experience the same degree of variability, so it is possible that some cells randomly modify their logical starting value, while others will start-up always at the same value. However, physically unclonable function applications, such as identification and key generation, require more constant logical starting value to assure high reliability in PUF response. For this reason, some kind of post-processing is needed to correct the errors in the PUF response. Unfortunately, those cells that have more constant logic output are difficult to be detected in advance. This work characterizes by simulation the start-up value reproducibility proposing several metrics suitable for reliability estimation during design phases. The aim is to be able to predict by simulation the percentage of cells that will be suitable to be used as PUF generators. We evaluate the metrics results and analyze the start-up values reproducibility considering different external perturbation sources like several power supply ramp up times, previous internal values in the bit-cell, and different temperature scenarios. The characterization metrics can be exploited to estimate the number of suitable SRAM cells for use in PUF implementations that can be expected from a specific SRAM design.[cat] En l’era de la Internet de les coses (IoT), garantir la seguretat del hardware ha esdevingut un tema de recerca crucial, en especial a causa de la creixent demanda de productes electrònics que es connecten remotament a través de xarxes. Per millorar la seguretat dels sistemes IoT, s’han proposat noves solucions hardware basades en la variabilitat dels processos de fabricació. Les funcions físicament inclonables (PUF) constitueixen una font fiable d’aleatorietat física i són una base essencial per a la seguretat hardware. Les memòries SRAM s’estan convertint en una de les alternatives més prometedores per a la implementació de funcions físicament inclonables encastades. Això és així ja que el valor d’encesa de cada una de les cel·les que formen els bits de la memòria depèn en gran mesura de la variabilitat pròpia del procés de fabricació. No tots els bits tenen el mateix grau de variabilitat, així que algunes cel·les canvien el seu estat lògic d’encesa de forma aleatòria entre enceses, mentre que d’altres sempre assoleixen el mateix valor en totes les enceses. No obstant això, les funcions físicament inclonables, que s’utilitzen per generar claus d’identificació, requereixen un valor lògic d’encesa constant per tal d’assegurar una resposta fiable del PUF. Per aquest motiu, normalment es necessita algun tipus de postprocessament per corregir els possibles errors presents en la resposta del PUF. Malauradament, les cel·les que presenten una resposta més constant són difícils de detectar a priori. Aquest treball caracteritza per simulació la reproductibilitat del valor d’encesa de cel·les SRAM, i proposa diverses mètriques per estimar la fiabilitat de les cel·les durant les fases de disseny de la memòria. L'objectiu és ser capaç de predir per simulació el percentatge de cel·les que seran adequades per ser utilitzades com PUF. S’avaluen els resultats de diverses mètriques i s’analitza la reproductibilitat dels valors d’encesa de les cel·les considerant diverses fonts de pertorbacions externes, com diferents rampes de tensió per a l’encesa, els valors interns emmagatzemats prèviament en les cel·les, i diferents temperatures. Es proposa utilitzar aquestes mètriques per estimar el nombre de cel·les SRAM adients per ser implementades com a PUF en un disseny d‘SRAM específic.[spa] En la era de la Internet de las cosas (IoT), garantizar la seguridad del hardware se ha convertido en un tema de investigación crucial, en especial a causa de la creciente demanda de productos electrónicos que se conectan remotamente a través de redes. Para mejorar la seguridad de los sistemas IoT, se han propuesto nuevas soluciones hardware basadas en la variabilidad de los procesos de fabricación. Las funciones físicamente inclonables (PUF) constituyen una fuente fiable de aleatoriedad física y son una base esencial para la seguridad hardware. Las memorias SRAM se están convirtiendo en una de las alternativas más prometedoras para la implementación de funciones físicamente inclonables empotradas. Esto es así, puesto que el valor de encendido de cada una de las celdas que forman los bits de la memoria depende en gran medida de la variabilidad propia del proceso de fabricación. No todos los bits tienen el mismo grado de variabilidad. Así pues, algunas celdas cambian su estado lógico de encendido de forma aleatoria entre encendidos, mientras que otras siempre adquieren el mismo valor en todos los encendidos. Sin embargo, las funciones físicamente inclonables, que se utilizan para generar claves de identificación, requieren un valor lógico de encendido constante para asegurar una respuesta fiable del PUF. Por este motivo, normalmente se necesita algún tipo de posprocesado para corregir los posibles errores presentes en la respuesta del PUF. Desafortunadamente, las celdas que presentan una respuesta más constante son difíciles de detectar a priori. Este trabajo caracteriza por simulación la reproductibilidad del valor de encendido de celdas SRAM, y propone varias métricas para estimar la fiabilidad de las celdas durante las fases de diseño de la memoria. El objetivo es ser capaz de predecir por simulación el porcentaje de celdas que serán adecuadas para ser utilizadas como PUF. Se evalúan los resultados de varias métricas y se analiza la reproductibilidad de los valores de encendido de las celdas considerando varias fuentes de perturbaciones externas, como diferentes rampas de tensión para el encendido, los valores internos almacenados previamente en las celdas, y diferentes temperaturas. Se propone utilizar estas métricas para estimar el número de celdas SRAM adecuadas para ser implementadas como PUF en un diseño de SRAM específico

Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neuronś firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron

Parametrization of stochastic inputs using generative adversarial networks with application in geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude