In this paper, a compact model for the negative bias temperature instability (NBTI) is developed by considering the interface-state generation and the hole-trapping mechanisms. This model shows accurate reproduction of the threshold voltage (V _th ) degradations measured from samples fabricated with different dielectric materials as well as processes. A total of eight model parameters are introduced for describing the different degradation origins. The parameter values are verified to exhibit universal properties as a function of the electrical field within the gate oxide (E _ox ). By implementing the universal NBTI model into the compact model HiSIM, the dynamic NBTI effect and circuit performance degradation can be predicted.

The Hough Transform (HT) is a method for extracting straight lines from an edge image. The main limitations of the HT for usage in actual applications are computation time and storage requirements. This paper reports a hardware architecture for HT implementation on a Field Programmable Gate Array (FPGA) with parallelized voting procedure. The 2-dimensional accumulator array, namely the Hough space in parametric form (ρ, θ), for computing the strength of each line by a voting mechanism is mapped on a 1-dimensional array with regular increments of θ. Then, this Hough space is divided into a number of parallel parts. The computation of (ρ, θ) for the edge pixels and the voting procedure for straight-line determination are therefore executable in parallel. In addition, a synchronized initialization for the Hough space further increases the speed of straight-line detection, so that XGA video processing becomes possible. The designed prototype system has been synthesized on a DE4 platform with a Stratix-IV FPGA device. In the application of road-lane detection, the average processing speed of this HT implementation is 5.4ms per XGA-frame at 200 MHz working frequency.

An experimental and theoretical study of real-time robot balancing on inclined surfaces with electrical feedback circuitry is presented. Force sensors are experimentally shown to extend the sustainability of a stable robot posture beyond a critical surface inclination. For this purpose, the inclination feedback from the force sensors is used to adjust the robot’s ankle-pitch-motor angle above the critical inclination, thus enabling the maintenance of a stable robot posture. Further, the Inverted Pendulum Model (IPM) 43-45 is extended to the case of inclined surfaces. Through application of this extended IPM it is demonstrated, that simultaneous use of gyro-sensor data can minimize the necessary initial adjustment of the motor angle for controlled robot-body rotation, which additionally has the positive effect of reducing possible overshoots of the motor’s rotation angle during feedback. Consequently, the reported feedback control improves the robot-body stability on inclined surfaces. Efficient implementation of the developed control scheme into an existing robot’s electrical system is proposed.

The compact model of HIroshima-University Starc Igfet Model (HiSIM)_GaN for GaN-HEMT devices is reported, which solves the Poisson equation iteratively, in a similar way as the industry-standard compact HiSIM models for other semiconductor devices. The model considers all possible charges induced within the device, including the dynamically varying trap density. It is verified that the model can reproduce the 2-D-device simulation results accurately. In particular, the operation frequency dependence of the current collapse can also be captured correctly based on the trap time constant.

Power dissipation of on-chip cache memories contributes a large portion of a processor's power consumption. Therefore, power management of cache memories is crucial in modern processors. On the other hand, bias temperature instability (BTI) is one of the most serious reliability concerns in SRAM-based on-chip memories. The effect of BTI on SRAM-cell transistors is manifested as their threshold-voltage shift over stress duration, which decreases the robustness of these structures. This paper presents a power consumption-reduction technique for data-cache memories, based on a storage management considering narrow-width values (NWVs), which additionally mitigates the BTI rate on the most aging susceptible SRAM cells of data-cache memory, as well. In the proposed technique, the most significant bits of data-cache memory words are stored in SRAM blocks that operate with lower VDD, to achieve the decrease of the related power consumption and aging rate. This is shown to reduce leakage-current and dynamic current of data-cache memory by 37.6% and 22.1%, respectively, besides improving its lifetime by up to 2.25x, all with negligible performance and area overheads.

Real-time surface recognition has become a critical factor for ensuring safe walking of intelligent biped robots in a complex human living environment. This work aims at enabling wide cost-efficient implementation of sensing solutions for surface recognition via walking-pattern classification by restricting the necessary hardware to a cost-economic microprocessor and a single type of force sensors. For experimental analysis, we explored the walking-pattern classification performance using a framework which combines a support vector machine (SVM) and four time-domain feature descriptors, i.e., mean of amplitude (MA), integral of absolute value (IAV), variance (VAR), and root mean square (RMS). During the online pattern classification, the dynamical force-sensory-data stream was extracted using a real-time overlapped-window-based method. Multiple binary SVM classifiers were applied for solving the multi-class classification problem, due to the reasonably high accuracy and the relatively small complexity for hardware implementation, allowing simultaneous strength exploitation of above four individual feature descriptors with a one-versus-one (OVO) strategy. The experimental results, obtained with 250 samples/surface, verified 93.8% mean average precision, 93.7% average accuracy and recall rates of 98.8%, 91.6%, 82.0%, 98.0%, 98.0% for smooth wood, rough foam, smooth foam, thick carpet, and thin carpet, respectively. Only the dynamical force-sensing data were employed for a 10-fold cross validation, which enabled the high processing speed of 0.73 ms/stride. The developed cost-efficient and accurate surface-recognition system can be useful for ensuring safe in-door locomotion for the biped robot and can help the robot to better understand the human living environment by increasing its sensing diversity.