Ali Shakouri

A woven structure includes thermoelectric ribbons interwoven with thread. Each thermoelectric ribbon includes a folded matrix of thermoelectric elements, the matrix having an insulating substrate that supports plural rows of thermoelectric elements, a plurality of conductive elements, and two terminals. The conductive elements form a series connection of the thermoelectric elements between the two terminals. A set of first conductive elements have a first temperature and a set of second conductive contacts have a second temperature lower than the first temperature when a first current flows in a first direction between the first matrix terminal and the second matrix terminal. The folded matrix is configured to form spaced-apart alternating stacks of the first conductive contacts and second conductive contacts. Each length of the yard or thread is interwoven such that it passes alternately under stacks of first conductive …

Systems and methods for detecting photothermal effect in a sample are described herein. In these systems and methods, a pump source is configured to generate a pump pulse train, a probe source is configured to generate a probe pulse train and is synchronized with the pump pulse train, and a camera collects the resulting data. The camera is configured to collect a first signal corresponding to a hot frame, wherein the hot frame includes visible probe beam as modified by a pump beam and a second signal corresponding to a cold frame, wherein the cold frame includes visible probe beam that has not been modified by a pump beam. A processor can subtract the second signal from the first signal to detect the photothermal effect.

Detecting electronic hot spots is important for understanding the heat dissipation and thermal management of electronic and semiconductor devices. Optical thermoreflective imaging is being used to perform precise temporal and spatial imaging of heat on wires and semiconductor materials. We apply quantum squeezed light to perform thermoreflective imaging on micro-wires, surpassing the shot-noise limit of classical approaches. We obtain a far-field temperature sensing accuracy of 42 mK after 50 ms of averaging and show that a pixel image can be constructed with such sensitivity in 10 minutes. We can further obtain single-shot temperature sensing of 1.6 K after only 10 of averaging enabling dynamical study of heat dissipation. Not only do the quantum images provide accurate spatio-temporal information about heat distribution, but the measure of quantum correlation provides additional information, inaccessible by classical techniques, that can lead to a better understanding of the dynamics. We apply the technique to both Al and Nb microwires and discuss the applications of the technique in studying electron dynamics at low temperatures.

Micrometeoroid and orbital debris (MMOD) are a key risk for spacecraft damage that could compromise missions. Detecting and evaluating MMOD damage is therefore a crucial component in the health monitoring of spacecraft, especially for long duration, deep space expeditions. In this work, we developed a passive sensor system fabricated from conductive metal ink screen-printed on flexible Kapton, using roll-to-roll manufacturing suitable for low-cost fabrication of large areas of sensors, as a MMOD sensor for a spacecraft shield. The sensor is integrated into a low density, two-wall Whipple shield comprising of thin aluminum sheets sandwiching a polyimide foam. The shield with the sensors were tested with hypervelocity impacts at approximately 7 km/s using different particle diameters. Data collected from the sensors were successfully used to determine the impact size, impact location, and predict the impact …

In order to train a learning-based prediction model, large datasets are typically required. One of the major restrictions of machine learning applications using customized databases is the cost of human labor. In the previous papers [3, 4, 5], it is demonstrated through experiments that the correlation between thin-film nitrate sensor performance and surface texture exists. In the previous papers, several methods for extracting texture features from sensor images are explored, repeated cross-validation and a hyperparameter auto-tuning method are performed, and several machine learning models are built to improve prediction accuracy. In this paper, a new way to achieve the same accuracy with a much smaller dataset of labels by using an active learning structure is presented.

Weather and soil conditions are particularly important when it comes to farming activities. Study of these factors and their role in nutrient and nitrate absorption rates can lead to useful insights with benefits for both the crop yield and the protection of the environment through the more controlled use of fertilizers and chemicals. There is a paucity of public data from rural, agricultural sensor networks. This is partly due to the unique challenges faced during the deployment and maintenance of IoT networks in rural agricultural areas. As part of a 5-year project called WHIN we have been deploying and collecting sensor data from production and experimental agricultural farms in and around Purdue University in Indiana. Here we release a dataset comprising soil sensor data from a representative sample of 3 nodes across 3 production farms, each for 5 months. We correlate this data with the weather data and draw some insights about the absorption of rain in the soil. We provide the dataset at: https://purduewhin.ecn.purdue.edu/dataset2021.

Data analytics and Artificial Intelligence (AI) have transformed many industries in the last decade. In tandem, a skilled workforce needs to understand how to gather/access data to extract trends and optimize operations, and how to label the key events and develop training data sets which can be used by machine learning (ML) experts for advanced analytics. The power of ML and AI has not been fully realized in the manufacturing sector. One of the major challenges is that the small and medium manufacturers which account for 98% of industry lack the dedicated data analytic workforce. This is combined with aging workers and significant challenges in hiring factory floor workers. To address this need, partnerships have been established between industry and academia through Wabash Heartland Innovation Network (WHIN) at Purdue University. In collaboration with Ivy Tech Community College, a series of workshops were developed to introduce data analytics, internet of things and basic machine learning concepts to local small and large manufacturing companies. This study will describe three short courses geared toward industry workers and professionals. The first short course is on the topic of energy savings and data analytics for Variable Frequency Drives (VFDs). The main goal of this workshop was to introduce electric motor data and VFDs for motor control to industry partners. Motors are a major source of energy consumption in manufacturing and other industries. In right applications, VFDs can reduce energy usage with relatively short return on investments. Using VFD or utilizing SMART motor overload devices, it is possible to gather …

Roll‐to‐Roll (R2R) manufacturing is changing how state‐of‐the‐art technology is made. Roll‐to‐Roll is part of a family of advanced scalable inline manufacturing techniques that make traditional and state‐of‐the‐art products at high yield and reduced cost. R2R manufacturing has grown rapidly and is fundamental to several advanced technologies such as functional materials and films, sensors for medicine, biology, and environment monitoring, energy harvesting devices, and flexible electronics. Advantages of R2R manufacturing include ease of machine design, adaptability to a wide range of additive processing methods, and very high throughput. To keep up with advances in R2R manufacturing, novel methods have emerged to measure important product metrics (thickness, roughness, conductivity) and monitor quality (uniformity, defects) inline and in real‐time. This review provides an overview of the state‐of …

In this paper, we study a food ingredient processing that has a total of ten fabrication stages, where the first stage is known as crystal seed count. In this stage, manufacturers study under a microscope the food sample and count crystal seeds per unit area. Sample preparation has imperfections (e.g. air bubbles) that need to be disregarded. The number of seed crystals per unit area is a key indicator for the quality of subsequent processing steps. This paper proposes a method for automating the crystal counting and air bubble removal processes in the seed count stage, in order to save manufacturers considerable time and money. An automated image processing pipeline for crystal counting and air bubble removal is employed in the proposed method. In addition to the proposed pipeline for crystal counting and air bubble removal automation, we also introduce an interactive GUI which is operated based on this …

Potentiometric ion-selective electrodes (ISEs) have broad applications in personalized healthcare, smart agriculture, oil/gas exploration, and environmental monitoring. However, high-precision potentiometric sensing is difficult with field-deployed sensors due to time-dependent voltage drift and the need for frequent calibration. In the laboratory setting, these issues are resolved by repeated calibration by measuring the voltage response at multiple standard solutions at a constant temperature. For field-deployed sensors, it is difficult to frequently interrupt operation and recalibrate with standard solutions. Moreover, the constant surrounding temperature constraint imposed by the traditional calibration process makes it unsuitable for temperature-varying field use. To address the challenges of traditional calibration for field-deployed sensors, in this study, we propose a novel in situ calibration approach in which we use …

Electrical and thermal co-design and co-optimization become more and more important for the state-of-the-art monolithic 3D integration. In this work, for the first time, we determined the thermal resistance (R TH ) and the thermal capacitance (C TH ) of back-end-of-line (BEOL) compatible atomic-layer-deposited (ALD) ultrathin In 2 O 3 field-effect transistors (FETs) by measuring the steady-state and transient temperatures of active devices using a thermo-reflectance (TR) imaging system. Through the extracted R TH and C TH , the heat dissipation capability of In 2 O 3 FETs is found to be related to the geometry of the transistors. An 83% reduction of R TH and a 379% increase of C TH can be obtained by scaling down the channel length (L ch ) of In 2 O 3 FETs from 6 μm to 600 nm. This work offers a new methodology to quantitatively study the thermal properties of thin film transistors along with their electrical …

In this work, the transient thermal and electrical characteristics of top-gated (TG), ultrathin, atomic-layer-deposited (ALD), back-end-of-line (BEOL) compatible indium oxide (In2O3) transistors on various thermally conductive substrates are co-optimized by visualization of the self-heating effect (SHE) utilizing an ultrafast high-resolution (HR) thermo-reflectance (TR) imaging system and overcome the thermal challenges through substrate thermal management and short-pulse measurement. At the steady-state, the temperature increase ( ) of the devices on highly resistive silicon (HR Si) and diamond substrates are roughly 6 and 13 times lower than that on a SiO2/Si substrate, due to the much higher thermal conductivities ( ) of HR Si and diamond. Consequently, the ultrahigh drain current ( ) of 3.7 mA/ at drain voltage ( ) of 1.4 V with direct current (dc) measurement is achieved with TG ALD In2O3 FETs on a diamond …

Optical signals carrying quantum correlations can be used to illuminate objects, enabling, in principle, quantum enhanced sensing of the object or its properties. Here, we demonstrate quantum enhanced temperature sensing of microelectronics using bright quantum optical signals. Relying on lock-in detection of thermoreflectivity, we measure the temperature change of a microwire induced by a current with an accuracy of better than 0.04∘ averaged over 0.1 s. The results show a nearly 50% improvement in accuracy compared to classical light of the same power and is a demonstration of below-shot-noise thermoreflectivity sensing. With moderate improvements, quantum temperature resolution of sub mK can be achieved at optical powers just below the laser-induced heating threshold, and thus the true quantum advantage of temperature sensing is within reach. Other sensing modalities can be adopted to extend …

In this work, we report the first concurrent use of electrical and thermal characterization, via thermoreflectance, to analyze Time Dependent Dielectric Breakdown in GaN MOSHEMTs. Electrically stressing the devices until a failure occurs, then evaluating them via thermoreflectance, revealed a geometric dependence of the failure mode. All soft breakdowns occurred at the mesa edge where the electric field strength was at its strongest, and tunneling current density was at its highest. This breakdown phenomenon at the mesa edge has been seen previously in GaN HEMTs employing a mesa architecture. Possible approaches to mitigate these failures in MOSHEMTs are proposed.

A sensor is disclosed which includes a piezoelectric layer, a piezoresistive layer, one or more electrode layers coupled to the piezoelectric layer and to the piezoresistive layer, the piezoelectric layer configured to provide an electrical signal in response to application of a dynamic disturbance, and the piezoresistive layer configured to provide a change in resis tivity in response to application of a static disturbance.

Using a four-wave mixing source of intensity-squeezed light, we perform thermoreflectivity measurement of a wire to determine the wire’s temperature with sub-shot-noise precision.

We present Monte-Carlo (MC) simulations and analyses of a newly proposed thermodynamic cycle in a solid-state regime, which is fundamentally apart from well-known solid-state thermoelectric or thermionic generators. The thermodynamic cycle is designed in analogy to the Otto cycle and includes compression, heating, and release of electronic gas. During the electron gas expansion, part of the energy is delivered to the load, and part of it is rejected to the lattice. Considering the non-equilibrium and the transient nature of the device, it is not limited by the Carnot efficiency. We show that thermal efficiencies higher than 70% are possible when the input heat is small. As we increase the input heat, the efficiency drops significantly and approaches a few percent due to larger electron-phonon interaction rates for higher energy electrons.

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.

For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system’s complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their …

Originally developed for use in controlled laboratory settings, potentiometric ion-selective electrode (ISE) sensors have recently been deployed for continuous, in situ measurement of analyte concentration in agricultural (e.g., nitrate), environmental (e.g., ocean acidification), industrial (e.g., wastewater), and health-care sectors (e.g., sweat sensors). However, due to uncontrolled temperature and lack of frequent calibration in these field applications, it has been difficult to achieve accuracy comparable to the laboratory setting. In this paper, we propose a novel temperature self-calibration method where the ISE sensors can serve as their own thermometer and therefore precisely measure the analyte concentration in the field condition by compensating for the temperature variations. We validate the method with controlled experiments using pH and nitrate ISEs, which use the Nernst principle for electrochemical sensing …