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A multiple-time programmable (MTP) twin-bit memory cell with metal floating gate coupled by n-well program gate is proposed and implemented by standard CMOS logic processes. Equipped with high programming gate coupling ratio controlled well by overlap area design, a twin bit cell with a pair of symmetric floating gates is successfully demonstrated. In addition, this memory also shows good endurance and low disturbance during the P/E cycling tests.

Integrity of rails is the foundation of safe rail transportation. It is critical to detect internal rail flaws in time, and one popular solution to this issue is ultrasonic techniques. On the other hand, long short-term memory (LSTM) has been proven in text classification to which we think the ultrasonic rail flaw detection can be quite similar. In this context, this paper proposes a novel text-style sequential modeling method for ultrasonic rail flaw data and a LSTM-based deep learning model for rail flaw detection. Comparative experiments proved the feasibility and remarkable computational efficiency of the proposed modeling method and model.

This paper discusses how to perform online integrity checking for a clock system spanning all over a heterogeneously integrated IC. A clock system in this work is assumed to consist of two major components - (1) the Delay-Locked Loop (DLL), commonly used for module-to-module clock synchronization, and (2) the clock distribution network (with clock buffers and interconnects). For the DLL part, we incorporate a “phase error monitoring scheme”, which is able to detect abnormal safety hazard, e.g., an instantaneous power glitch. For the clock distribution network, we incorporate a periodic self-test scheme featuring a “special short-pulse driven flush test procedure” to detect any worsening Clock Delay Fault (CDF). The proposed method can help identify a failure threat before it strikes havoc. Post-layout simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Traditionally, it has been a difficult task to characterize the quality of a Clock Delay Fault (CDF). Here, a CDF is referred to a delay fault occurring in the clock network that causes an abnormal delay when a clock signal travels through it and thereby causing large-than-expected clock skews at the clock ports of some flip-flops. In a recent work [12], a modified flush test procedure taking short pulses as the test stimuli at selected clock cycles has been proven effective in characterizing a CDF. In this work, we extend this technique to support online Built-In Self-Test (BIST). We investigate two fault detection strategies, namely, valid-range criterion, and valid-span criterion, and we compare their fault detection abilities in terms of the minimum detectable CDF. With the proposed BIST scheme, the health condition of the clock network in a device operating in the field can be inspected on a regular basis so as to take precautions before the clock network breaks down due to deteriorating fault effects.

With the rise of the Internet industry and the technique of artificial intelligence, personalized services are increasingly important in recent years for improving user experience and increasing corporates' competitiveness and profits. Precise prediction of customers' behaviors has shown great effects in modern business marketing, especially when making personalized decisions. In this paper, we develop a deep learning network to make personalized predictions of their behaviors among a list of potential choices. The architecture of this model combines each user's features and his historical event lists by sequence-to-sequence (Seq2Seq) structure and make predictions based on his recent event lists. We also modify the long-short- term memory (LSTM) cell forget gate's structure to enhance the attention ability. Such design, called the attetioned LSTM, converges quicker and better while still maintain the similar performance in open dataset IMDB. In addition, in dealing with personalized prediction problems in real-world datasets provided by our cooperative company, our attentioned LSTM achieves a 10% higher precision in average than the standard LSTM model. The advantage is confirmed in evaluation of this generic method on a real dataset of users' behaviors sequences and individuals' attribute profiles from Ant Financial. It also achieves a great result working on the real-world business scene. This model can also achieve a great performance working on the real-world business scene.

Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site are sometimes incomplete but provide important clues for weather prediction at nearby sites, which are not fully exploited in existing work yet. To solve this problem, we propose a multi-task convolutional neural network model to automatically extract features from the time series measured at observation sites and leverage the correlation between the multiple sites for weather prediction via multi-tasking. To the best of our knowledge, this is the first attempt to use multi-task learning and deep learning techniques to predict short-term rainfall amount based on multi-site features. Specifically, we formulate the learning task as an end-to-end multi-site neural network model which allows to leverage the learned knowledge from one site to other correlated sites, and model the correlations between different sites. Extensive experiments show that the learned site correlations are insightful and the proposed model significantly outperforms a broad set of baseline models including the European Centre for Medium-range Weather Forecasts system (ECMWF).

A FinFET SCR embedded ESD clamp for power supply protection with low leakage current is demonstrated. The proposed clamp is suitable for low power applications since it reduces ∼87% of leakage current per bigFET width and improves ESD robustness to ∼2X per footprint compared to the conventional RC-triggered clamp.

All electronic packages involve with a multi-material system, in which many of the materials or films are susceptible to moisture absorption. Despite dissimilar material properties, moisture transports in a multi-material system from a high "water activity" region to a low one, where water activity is a measure of water energy in a specific substance. This, however, has not been well recognized in electronic packaging industry. Furthermore, moisture concentration gradient is often deemed as the driving force for moisture transport, which inevitably poses a challenging discontinuity issue at interface for moisture diffusion in multi-material systems. Even though several normalization schemes have been developed in the literature, much confusion has existed on the fundamental principle of moisture diffusion. This paper derived an activity-based diffusion model using the concepts of chemical potential and water activity. We showed that the continuity of water activity at interface in dissimilar materials is warranted, and furthermore, many nonlinear water sorption isotherms can be applied in the activity-based model by using a new property called "generalized solubility". The activity-based model thus is capable to study complex moisture diffusion in multi-material system. Moreover, in this paper, the activity-based model was used to unify the different normalization theories, such as solubility-based and the so-called wetness normalization approaches. We also discussed how water sorption isotherm would affect the conventional moisture diffusivity, finding that only for some limiting cases (e.g., Henry sorption isotherm), the "effective moisture diffusivity" becomes independent of moisture concentration. We pointed out that the generalized solubility that are needed to solve the diffusion can be obtained using conventional terms such as saturated moisture concentration and solubility. As demonstration, a numerical example was performed in commercial finite element software to study the moisture diffusion through a bi-material interface under dynamic temperature and humidity conditions. The results from different nonlinear sorption isotherms were compared to demonstrate the capability and versatility of the model. We concluded that the activity-based moisture diffusion model is a unified and versatile approach to study and understand the moisture diffusion mechanism in IC packages.

ITΌ-free inverted SMPV1:PC 71 BM solar cells with an Al doped ZnO (AZO) transparent electrodes are fabricated. The AZO thin film prepared by pulsed laser deposition (PLD) technique exhibits high transmission (>85%) and low sheet resistance (∼30 Ω/sq). The absorption spectrums of devices are also simulated by RCWA method to optimize the performance of photocurrent response. The power conversion efficiency of ITO-free inverted small molecule solar cell can reach 4.03% which is close to that of ITO based small molecule solar cell.

An optimized SCR structure was proposed for high turn-on speed and low parasitic capacitance in FinFET CMOS process. Experimental results indicate that the proposed SCR structure delivers the best known results among the literatures (140mA/fF). By adopting the structure, ESD protection design for multi Gb/s transceiver can be simply realized.