Sharif Digital Repository

The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this paper, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforces the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network to fulfill the data diversity. Outcomes show a... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this article, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforce the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network (CDNN) to fulfill the data diversity. Outcomes... 

Compressive strength, a crucial mechanical property of cement mortars, is typically measured destructively. However, there is a need to evaluate the strength of unique cement-based samples at various ages without causing damage. In this paper, a predictive framework using a genetic algorithm (GA) is proposed for estimating the compressive strength of ordinary cement-based mortars based on their dynamic elastic modulus, measured non-destructively using the impulse excitation technique. By combining the Popovics model (PM) and the Lydon-Balendran model (LBM), the static elastic modulus of samples was calculated using constant coefficients, representing an equivalent compressive strength. A GA... 

The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (Trg) and critical thickness (Dmax). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To... 

Atomic force microscopy (AFM) is an efficient tool for studying the structural heterogeneity in metallic glasses (MGs). However, time-consuming analysis and limitations in the scanning process are downsides of this experiment. To tackle these problems, a machine learning (ML) model was developed to predict the distribution of energy dissipation on the MG surface with the increase in number of AFM scanning. The results indicated that it was possible to accurately predict the energy of scanning points, leading to a timesaving and reliable study. Moreover, characterization of structural heterogeneity shows that the viscoelastic response of each nanoscale region under sequences of AFM scans... 

This study tries to establish a powerful machine learning (ML) model for predicting the compressive strength of cement-based mortars by using dynamic elasticity data. The ML model was developed on the basis of Bayesian theorem, leading to a decrease in the overfitting problem compared with other conventional neural networks. Moreover, for the first time, the empirical equations were embedded in the ML model, enhancing the correlation between dynamic elasticity and compressive strength in the cement-based mortars. The results showed that the ML model efficiently predicted the compressive strength with determination coefficient (R2) of 95.2% and root mean square error (RMSE) of 0.0488 for... 

In this paper, having investigated the behavior of GPGPU applications, we present an effcient L2 cache architecture for GPUs based on STT-RAM technology. With the increase of processing cores count, larger on-chip memories are required. Due to its high density and low power characteristics, STT-RAM technology can be utilized in GPUs where numerous cores leave a limited area for on-chip memory banks. They have however two important issues, high energy and latency of write operations, that have to be addressed. Low data retention time STT-RAMs can reduce the energy and delay of write operations. However, employing STT-RAMs with low retention time in GPUs requires a thorough investigation on... 

Future GPUs should have larger L2 caches based on the current trends in VLSI technology and GPU architectures toward increase of processing core count. Larger L2 caches inevitably have proportionally larger power consumption. In this article, having investigated the behavior of GPGPU applications, we present an efficient L2 cache architecture for GPUs based on STT-RAM technology. Due to its high-density and low-power characteristics, STT-RAM technology can be utilized in GPUs where numerous cores leave a limited area for on-chip memory banks. They have, however, two important issues, high energy and latency of write operations, that have to be addressed. Low retention time STT-RAMs can... 

There exists an extensive range of applications for elevated gain DC/DC converters, as numerous low-voltage resources are exploited for power supply. Therefore, this study introduces a groundbreaking magnetically coupled DC/DC converter specifically designed for resources with low voltage, including micro PV or fuel cell systems. By enduring low current and voltage stresses, the power devices in this converter ensure remarkable efficiency while maintaining proven voltage ratio capability. The operational principles of the converter are thoroughly discussed and supported by the implementation of a 200W-400V prototype. In order to confirm the effectiveness of the converter, a range of... 

Depending on the application in which power electronic converters (PECs) are deployed, failure processes may endanger the desirable performance of PECs. This paper offers holistic insights on reliability modeling of PECs considering dependencies in two simultaneous failure processes, namely gradual wearing-out degradation and vibration sudden degradation. While sudden and gradual degradation processes may individually affect the useful lifetime of PECs, their mutual interdependencies could significantly accelerate the aging mechanisms. A new analytical model for reliability assessment of PECs is proposed that can capture such mutual interdependence of simultaneous failure processes. The... 

Using multiple antennas at the transmit and receive sides of a passive radar brings both the benefits of MIMO radar and passive radar. However one of the obstacles arisen in such configuration is the receive antennas placement in proper positions so that the radar performance is improved. Here we just consider the case of positioning one receiver among multiple illuminators of opportunity. Indeed it is a start for the solution of optimizing the geometry of the multiple receivers in a passive radar  

SbCl3 supported on montmorillonite K-10 is an efficient catalyst for the ring opening of epoxides with aromatic amines under solvent-free conditions and microwave irradiation to give the corresponding b-amino alcohols in high yields with high regioselectivity  

In this paper, we consider an integrated sensing and communication (ISAC) system with wireless power transfer (WPT) where an unmanned aerial vehicle (UAV)-based radar serves a group of energy-limited communication users in addition to its sensing functionality. In this architecture, the radar senses the environment in phase 1 (namely sensing phase) and mean-while, the communications users (nodes) harvest and store the energy from the radar transmit signal. The stored energy is then used for information transmission from the nodes to UAV in phase 2, i.e., uplink phase. Performance of the radar system depends on the transmit signal as well as the receive filter; the energy of the transmit... 

One of the important obstacles in MIMO (Multiple Input Multiple Output) radars is the issue of designing proper transmit signals. Indeed, the capability of signal design is a significant advantage in MIMO radars, through which, the system can achieve much better performance. Many different aspects of this performance improvement have been considered yet, and the transmit signals have been designed to attain such goal, e.g., getting higher SNR or better detector's performance at the receiver. However, an important tool for evaluating the radar's performance is its ambiguity function. In this paper, we consider the problem of transmit signal design, in order to optimize the ambiguity function... 

Two gains play key roles in recently developed MIMO wireless communication systems: "spatial diversity" gain and "spatial multiplexing" gain. The diversity gain refers to the capability to decrease the error rate of the MIMO channel, while the multiplexing gain implicitly refers to the amount of increase in the capacity of the MIMO channel. It has been shown that there is a fundamental tradeoff between these two types of gains, meaning interplay between increasing reliability (via an increase in the diversity gain) and increasing data rate (via an increase in the multiplexing gain). On the other hand, recently, MIMO radars have attracted much attention for their superior ability to enhance... 

It is a well known fact that using multiple antennas at transmit and receive sides improves the detection performance. However, in such multiple-input multiple-output (MIMO) configuration, proper positioning of transmitters and receivers is a big challenge that can have significant influence on the performance of the overall system. In addition, determining the power of each transmitter under a total power constraint is a problem that should be solved in order to enhance the performance and coverage of such a system. In this paper, we design the Neyman-Pearson detector under the Rayleigh scatter model and use it to introduce a criterion for the antenna placement at both transmit and receive... 

There has been much interest, recently, towards exploiting the Multiple-Input Multiple-Output (MIMO) technique in radar. It is shown that using multiple antennas at transmit and receive sides can improve the performance of the system. However, in order to analyze the system's performance, its ambiguity function, i.e. the ambiguity function of a MIMO radar, is needed to be defined. In this paper, beginning from the information theoretic definitions, we derive such function, specifically for a MIMO radar with widely separated antennas