Parameter Variation Sensing and Estimation in Nanoscale Fabrics

Parameter variations introduced by manufacturing imprecision are becoming more influential on circuit performance. This is especially the case in emerging nanoscale fabrics due to unconventional manufacturing steps (e.g., nano-imprint) and aggressive scaling. These parameter variations can lead to performance deterioration and consequently yield loss. Parameter variations are typically addressed pre-fabrication with circuit design targeting worst-case timing scenarios. However, this approach is pessimistic and much of performance benefits can be lost. By contrast, if parameter variations can be estimated post-manufacturing, adaptive techniques or reconfiguration could be used to provide more optimal level of tolerance. To estimate parameter variations during run-time, on-chip variation sensors are gaining in importance because of their easy implementation. In this thesis, we propose novel on-chip variation sensors to estimate variations in physical parameters for emerging nanoscale fabrics. Based on the characteristics of systematic and random variations, two separate sensors are designed to estimate the extent of systematic variations and the statistical distribution of random variations from measured fall and rise times in the sensors respectively. The proposed sensor designs are evaluated through HSPICE Monte Carlo simulations with known variation cases injected. Simulation results show that the estimation error of the systematic-variation sensor is less than 1.2% for all simulated cases; and for the random-variation sensor, the worst-case estimation error is 12.7% and the average estimation error is 8% for all simulations. In addition, to address the placement of on-chip sensors, we calculate sensor area and the effective range of systematic-variation sensor. Then using a processor designed in nanoscale fabrics as a target, an example for sensor placement is introduced. Based on the sensor placement, external noises that may affect the measured fall and rise times of outputs are identified. Through careful analysis, we find that these noises do not deteriorate the accuracy of the systematic-variation sensor, but affect the accuracy of the random-variation sensor. We believe that the proposed on-chip variation sensors in conjunction with post-fabrication compensation techniques would be able to improve system-level performance in nanoscale fabrics, which may be an efficient alternative to making worst-case assumptions on parameter variations in nanoscale designs

SYNTHESIS AND CHARACTERIZATION OF NANOSCALE FLUORESCENT POLYMER FILMS

Performance of the chemical sensing devices is based sensitivity to the targeted analytes and accuracy of the measurements. Of many different types of chemical sensors, fluorescence based sensing devices draw much attention as efficient yet small and straightforward devices, capable of detection of various analytes at extremely small quantities. Polymeric materials provide support for the fluorescent materials and usually serve as a host for the chromophores, protecting them from harsh environments, and serve as a transport media for the analyte. In the present study thin fluorescent polymer films, sensitive to the environment are synthesized and studied. Poly(glycidyl methacrylate) was used as a host material and Rhodamine B (RhB) was used as a fluorescent material. PGMA-RhB polymer films deposited on the flat surface of silicon wafers and glass slides showed the ability to uptake vapor of organic solvents from the vapor phase. It was observed, that during the swelling of the film fluorescent emission of the RhB molecules changes. Those changes were attributed to the variations in the RhB immediate environment, therefore confirming the sensitivity of the PGMA-RhB layer to the environmental changes. The fluorescent response of the PGMA-RhB layer was unique for each analyte used in the study and could be tuned by grafting of non-fluorescent layer to the surface of PGMA-RhB. Four different polymers (polystyrene, poly(2-vinyl pyridine), polyacrylic acid, and polyethylene glycol) were grafted to the surface of PGMA-RhB and each of the polymers grafted induced unique changes of fluorescent response of the PGMA-RhB. The attachment of the sensitive fluorescent layers to the surface of silica nanoparticles and fibers of Polyethylene terephthalate fabrics was performed. The sensitivity of the PGMA-RhB layers of structures with high roughness was improved, while the ability to tune the fluorescent response of the system was retained. Overall the studies have shown, that proposed method of fluorescent response tuning could be used in sensing applications

Physically Equivalent Intelligent Systems for Reasoning Under Uncertainty at Nanoscale

Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds on a probabilistic information representation and multi-domain mixed-signal circuit style, and is tightly coupled to a nanoscale physical layer that spans magnetic and electrical domains. Based on bottom-up device-circuit-architecture simulations, we show up to four orders of magnitude performance improvement (using computational resolution of 0.1) vs. best-of-breed multi-core machines with 100 processors, for BNs with about a million variables. Smaller problem sizes of ~100 variables can be realized at 20 mW power consumption and very low area around a few tenths of a mm2. Our vision is to enable solving complex Bayesian problems in real time, as well as enable intelligence capabilities at a small scale everywhere, ushering in a new era of machine intelligence

Printable stretchable interconnects

This article presents recent progress and a comprehensive overview of stretchable interconnects based on printable nanocomposites. Nanocomposite-based inks for printed stretchable interconnects have been categorized according to dispersed filler materials. They comprise of carbon-based fillers and metal-based fillers. Benefits in terms of excellent electrical performance and elastic properties make nanocomposites the ideal candidates for stretchable interconnect applications. Deeper analysis of nanocomposites-based stretchable interconnects includes the correlation between the size of fillers, percolation ratio, maximum electrical conductivity and mechanical elasticity. The key trends in the field have been highlighted using curve fitting methods on large data collected from the literature. Furthermore, a wide variety of applications for stretchable interconnects are presented

Wearable sensors for respiration monitoring: a review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version

MICROCANTILEVER-BASED FORCE SENSING, CONTROL AND IMAGING

This dissertation presents a distributed-parameters base modeling framework for microcantilever (MC)-based force sensing and control with applications to nanomanipulation and imaging. Due to the widespread applications of MCs in nanoscale force sensing or atomic force microscopy with nano-Newton to pico-Newton force measurement requirements, precise modeling of the involved MCs is essential. Along this line, a distributed-parameters modeling framework is proposed which is followed by a modified robust controller with perturbation estimation to target the problem of delay in nanoscale imaging and manipulation. It is shown that the proposed nonlinear model-based controller can stabilize such nanomanipulation process in a very short time compared to available conventional methods. Such modeling and control development could pave the pathway towards MC-based manipulation and positioning. The first application of the MC-based (a piezoresistive MC) force sensors in this dissertation includes MC-based mass sensing with applications to biological species detection. MC-based sensing has recently attracted extensive interest in many chemical and biological applications due to its sensitivity, extreme applicability and low cost. By measuring the stiffness of MCs experimentally, the effect of adsorption of target molecules can be quantified. To measure MC\u27s stiffness, an in-house nanoscale force sensing setup is designed and fabricated which utilizes a piezoresistive MC to measure the force acting on the MC\u27s tip with nano-Newton resolution. In the second application, the proposed MC-based force sensor is utilized to achieve a fast-scan laser-free Atomic Force Microscopy (AFM). Tracking control of piezoelectric actuators in various applications including scanning probe microscopes is limited by sudden step discontinuities within time-varying continuous trajectories. For this, a switching control strategy is proposed for effective tracking of such discontinuous trajectories. A new spiral path planning is also proposed here which improves scanning rate of the AFM. Implementation of the proposed modeling and controller in a laser-free AFM setup yields high quality image of surfaces with stepped topographies at frequencies up to 30 Hz. As the last application of the MC-based force sensors, a nanomanipulator named here MM3A® is utilized for nanomanipulation purposes. The area of control and manipulation at the nanoscale has recently received widespread attention in different technologies such as fabricating electronic chipsets, testing and assembly of MEMS and NEMS, micro-injection and manipulation of chromosomes and genes. To overcome the lack of position sensor on this particular manipulator, a fused vision force feedback robust controller is proposed. The effects of utilization of the image and force feedbacks are individually discussed and analyzed for use in the developed fused vision force feedback control framework in order to achieve ultra precise positioning and optimal performance