Reverse‐Conducting IGBT Using MEMS Technology on the Wafer Back Side

In this paper, we present a 600‐V reverse conducting insulated gate bipolar transistor (RC‐IGBT) for soft and hard switching applications, such as general purpose inverters. The newly developed RC‐IGBT uses the deep reactive‐ion etching trench technology without the thin wafer process technology. Therefore, a freewheeling diode (FWD) is monolithically integrated in an IGBT chip. The proposed RC‐IGBT operates as an IGBT in forward conducting mode and as an FWD in reverse conducting mode. Also, to avoid the destructive failure of the gate oxide under the surge current and abnormal conditions, a protective Zener diode is successfully integrated in the gate electrode without compromising the operation performance of the IGBT.     

Design of a Temporal Learning Chip for Signal Generation and Classification

The design and analog VLSI implementation of a recurrent neural network with integrated temporal learning is presented. The learning algorithm is forward in time, and is implemented strictly as instantaneous, local weight updates. PSpice simulations of networks with 4 to 6 neurons demonstrate robust learning of trajectory generation and classification tasks. A scalable 2-D VLSI architecture is described and a prototupe 4-neuron recurrent neural network with learning has subsequently been fabricated in MOSIS TinyChip 2 micron technology. Experimental results of the chip validate the learning performance with convergence in the millisecond range. Specific experimental results of learning circular and figure-8 dynamic trajectories are included.     

Implementation and characterization of flash-based hardware security primitives for cryptographic key generation

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off-the-shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware-based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min-entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high-entropy requirements, such as cryptographic key generation for IoT devices.     

Resource Allocation with Partitioning Criterion for Macro-Femto Overlay Cellular Networks with Fractional Frequency Reuse

The interference mitigation technique based on fractional frequency reuse (FFR) provides improved cell-edge performance with similar overall cell capacity as that of systems with the frequency reuse factor of one. Furthermore, frequency sub-band allocation by FFR has the benefit of allowing flexibility for the deployment of femto-cells through frequency partitioning. Determination of a proper frequency partitioning criterion between the cell-center and the cell-edge, and between the cells with femto-cells is an important issue. In addition, time resource partitioning introduces another degree of freedom to the design of time-frequency resource allocation. In this paper, we propose a novel time-frequency resource allocation mechanism using FFR for a macro-femto overlay cellular network. Feasible frequency sub-band and time resource is allocated to the cell-center and the cell-edge region in a cell by the proposed partitioning criterion and the time partitioning ratio. We provide a guideline for how to determine the partitioning criterion for the regions and how to design the amount of time resource. We derive the average capacity of macro-cells and femto-cells, and introduce a new harmonic mean metric to maximize the average capacity of the regions while achieving the fairness among users in a cell.     

Relay-volunteered multi-rate cooperative MAC protocol for IEEE 802.11 WLANs

In IEEE 802.11, the rate of a station (STA) is dynamically determined by link adaptation. Low-rate STAs tend to hog more channel time than high-rate STAs due to fair characteristics of carrier sense multiple access/collision avoidance, leading to overall throughput degradation. It can be improved by limiting the transmission opportunities of low-rate STAs by backoff parameters. This, however, may cause unfair transmission opportunities to low-rate STAs. In an attempt to increase overall throughput by volunteer high-rate relay STAs while maintaining fairness, we propose a new cooperative medium access control (MAC) protocol, relay-volunteered multi-rate cooperative MAC (RM-CMAC) based on ready to send/clear to send in multi-rate IEEE 802.11. In the RM-CMAC protocol, we show that the effect of hogging channel time by low-rate STAs can be remedied by controlling the initial backoff window size of low-rate STAs and the reduced transmission opportunity of low-rate STAs can be compensated by the help of volunteer high-rate relay STAs. We analyze the performance of RM-CMAC, i.e., throughput and MAC delay, by a multi-rate embedded Markov chain model. We demonstrate that our analysis is accurate and the RM-CMAC protocol enhances the network throughput and MAC delay while maintaining the fairness of low-rate STAs.     

Pilot-Assisted Carrier Frequency Offset Estimation for OFDM systems with Multiple Preambles Over Fast Fading Channels

This paper proposes a carrier frequency offset (CFO) estimation scheme for OFDM systems over fast fading channels. In the proposed scheme, exploiting the multiple preambles with the identical values, we estimate the CFO over fast fading channels. In particular, we improve the performance of the CFO estimator by applying the overlapped windows to preambles. Through simulations, we validate the proposed estimation scheme by showing the effectiveness of the proposed estimator.     

Tuning Interfacial Properties by Spontaneously Generated Organic Interlayers in Top‐Contact‐Structured Organic Transistors

Controlling the interfacial properties between the electrode and active layer in organic field‐effect transistors (OFETs) can significantly affect their contact properties, resulting in improvements in device performance. However, it is difficult to apply to top‐contact‐structured OFETs (one of the most useful device structures) because of serious damage to the organic active layer by exposing solvent. Here, a spontaneously controlled approach is explored for optimizing the interface between the top‐contacted source/drain electrode and the polymer active layer to improve the contact resistance (R_C). To achieve this goal, a small amount of interface‐functionalizing species is blended with the p‐type polymer semiconductor and functionalized at the interface region at once through a thermal process. The R_C values dramatically decrease after introduction of the interfacial functionalization to 15.9 kΩ cm, compared to the 113.4 kΩ cm for the pristine case. In addition, the average field‐effect mobilities of the OFET devices increase more than three times, to a maximum value of 0.25 cm² V^?1 s^?1 compared to the pristine case (0.041 cm² V^?1 s^?1), and the threshold voltages also converge to zero. This study overcomes all the shortcomings observed in the existing results related to controlling the interface of top‐contact OFETs by solving the discomfort of the interface optimization process.     

Development of a robotic system for the bed-ridden

The ability to live without the help of a caretaker enhances the quality of life of those who are bed-ridden or confined to a wheel-chair. A lot of systems have been proposed to support the daily life of bed-ridden. A smart home, especially a bed-type robot system, can help the movement and action of patients.This paper discusses the development of an intelligent bed robot system (IBRS), which can help the elderly and the disabled have independent life in bed. The IBRS is a special bed equipped with two robot arms and an array of pressure sensors attached to the mattress. The pressure distribution on the mattress is used to estimate the pose of the patient, and an appropriate assistance is provided by the robot arms. The proposed system has been tested experimentally for its effectiveness while maximizing the therapeutic outcome.     

The effects of bath composition on the morphologies of electroless nickel under-bump metallurgy on Al input/output pad

The electroless nickel plating on an aluminum input/output (I/O) pad was investigated. The aluminum pad was pretreated in a zincate solution prior to electroless nickel plating. Zinc particles on the aluminum pad gave a good adherent nickel layer. The adhesion and uniformity of zinc on the aluminum is the key factor in under-bump metallurgy (UBM). The electrode potential changes with and without zinc ions in the bath were measured to analyze the sequence of two competing reactions: zinc deposit and hydrogen evolution. The relationship between aluminum dissolution and the ratio of zinc and NaOH was investigated. The electroless nickel deposition rate was dependent on bath composition. The effects of complexing ligand and additive on the nickel deposit were analyzed. Electrode potential changes were measured with time to confirm nucleation and grain growth. Adhesion of the UBM was related to zinc-particle dissolution and nickel nucleation. The uniform nickel UBM was fabricated on a real Al I/O pad.     

The Design of Genetically Optimized Self-Organizing Neural Networks with Polynomial and Fuzzy Polynomial Neurons

In this study, we introduce and investigate a class of neural architectures of self-organizing neural networks (SONNs) that is based on a genetically optimized multilayer perceptron with polynomial neurons (PNs) or fuzzy polynomial neurons (FPNs), develop a comprehensive design methodology involving mechanisms of genetic optimization, and carry out a series of numeric experiments. We distinguish between two kinds of SONN architectures: (a) PN-based and (b) FPN-based SONNs. The augmented genetically optimized SONN (gSONN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one encountered in the conventional SONN. The genetic algorithm (GA)-based design procedure being applied at each layer of SONN leads to the selection of preferred nodes (PNs or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) available within the network.