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Kulicke and Soffa Industries, Inc. have announced the appointment of Dr Arthur J. Schneider as Vice President of Research and Development. Dr Schneider is based in Willow Grove and reports directly to Donald R. VanLuvanee, K & S President.

Reliability of RAM memory systems is impaired by environmental disturbances, causing soft errors, whereby one data bit is transformed to another bit. Single‐error correcting codes with memory scrubbing offer the most effective method to recover from such errors. This paper analyzes the reliability and determines the MTTF for simplex and duplex memory systems with single‐error correction and/or soft‐error scrubbing recovery. It extends previous work on the deterministic scrubbing recovery of simplex memory systems by using a more general model that takes into account cancelling soft errors. In the duplex memory system an additional level of static redundancy is proposed by employing a decoding algorithm at the memory module level. The reliability analysis of the duplex system with soft‐error scrubbing takes into account the decoder output which upon scrubbing transforms words with a number of multiple errors to words with a different number of errors. Computer results show that this combination of data and system redundancy provides more reliability than either data or system redundancy alone.

With continuous scaling of digital circuit CMOS technology, the vulnerability of these circuits are significantly increasing against the soft errors. On the other hand, the effects of process variation in the electrical properties of nano-scale circuits, have introduced the statistical methods as an unavoidable choice for the soft error rate (SER) estimation. The purpose of this paper is to provide a statistical soft error rate (SSER) estimation approach for combinational circuits in the presence of process variation.

In this paper a new method is proposed for the SSER estimation of combinational circuits based on the Bayesian networks (BNs). This allows to factor the joint probability distributions over variables in a circuit graph. The distribution of the initial transient fault pulse is estimated by the pre-characterization tables. Timing signals are propagated by BN theory and the probability distribution of electrical and timing masking are calculated.

Simulation results for some benchmark circuits show that the proposed method is accurate with 3.7 percent difference with the Monte-Carlo SPICE simulation and with orders of magnitude improvement in runtime.

The proposed framework is the scheme giving the low estimation time with plausible accuracy compared to other schemes. The comparison exhibits that the designer can save its estimation time in terms of performance and complexity. The deterministic-based methods also are able to evaluate the SER of combinational circuit, yet in an unacceptable time.

In spite of the vehicle, magnetic field interference can be reduced by some measures and techniques in ammunition design and manufacturing stage, the corruption of the vehicle magnetic field can still reach hundreds to thousands of nanoteslas. Besides, the magnetic field that the ferromagnetic materials generate in response to the strong magnetic field in the vicinity of the body. So, a real-time and accurate vehicle magnetic field calibration method is needed to improve the real-time measurement accuracy of the geomagnetic field for spinning projectiles.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time. In the method, the elliptical model of magnetometer measurement is established to convert the coefficients of hard and soft iron errors into the parameters of the elliptic equation. Then, the parameters are estimated by recursive least square estimator in real-time. Finally, the initial conditions for the estimator are established using prior knowledge method or static calibration method.

Studies show the proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real-time. A turntable experiments indicate that the post-calibration residuals approximate the measurement noise of the magnetometer and the roll accuracy is better than 1°. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers' calibration in real-time.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time and the calculation is small. Besides, it does not take up storage space. The proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real time. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers’ calibration in real-time.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

The purpose of this paper is to estimate the deformation of soft manipulators caused by obstacles accurately and the contact force and workspace can be also predicted.

The continuum deformation of the backbone of the soft manipulator under contact is regarded as two constant curvature arcs and the curvatures are different according to the fluid pressure and obstacle location based on piecewise constant curvature framework. Then, this study introduces introduce the moment balance and energy conservation equation to describe the static relationship between driving moment, elastic moment and contact moment. Finally, simulation and experiments are carried out to verify the accuracy of the proposed model.

For rigid manipulators, environmental contact except for the manipulated object was usually considered as a “collision” which should be avoided. For soft manipulators, an environment is an important tool for achieving manipulation goals and it might even be considered to be a part of the soft manipulator’s end-effector in some specified situations.

There are also some limitations to the presented study. Although this paper has made progress in the static modeling under environmental contact, some practical factors still limit the further application of the model, such as the detection accuracy of the environment location and the deformation of the contact surface.

Based on the proposed kinematic model, the bending deformation with environmental contact is discussed in simulations and has been experimentally verified. The comparison results show the correctness and accuracy of the presented SCC model, which can be applied to predict the slender deformation under environmental contact without knowing the contact force.

The purpose of this paper is to present a transition detector (TD)-based radiation hardened flip-flop (TDRH-FF) for single event upset (SEU).

With SEU recovery and single event transient (SET) detector mechanism, the TDRH-FF can tolerate SEU during hold mode and generate a warning signal for architecture-level recovery during transport mode when input signal contains SET. Evaluation results show that the TDRH-FF outperforms comparable comprehensive performance.

Simulation results show that 1) the mean pulse width of the correction glitches (at full width half maximum) of TDRH-FF is less than 10 ps; 2) the area overhead of TDRH-FF is similar to the EVFERST-FF, BISER-FF and DNURHL-FF; 3) TDRH-FF has the same average power consumption as SETTOF, and moderate PDP and Ps values among these compared FFs.

In this paper, a TD-based TDRH-FF is proposed to solve the problems in the previous design. And the main contributions of the proposed TDRH-FF are summarized: Minimum size transistors are used in the proposed TD which leads to a considerable decrease in area overheads and propagation delay (resulting in an ignorable correction glitch); and compared with other radiation hardened flip-flop, TDRH-FF outperforms comparable comprehensive performance.

The purpose of this paper is to reduce parity generation latency if the input value is narrow.

Soft errors caused by cosmic particles and radiation emitted by the packaging are important problems in contemporary microprocessors. Parity bits are used to detect single bit errors that occur in the storage components. In order to implement parity logic, multiple levels of XOR gates are used and these XOR trees are known to have high delay. Many produced and consumed values inside a processor hold consecutive zeros and ones in their upper order bits. These values can be represented with less number of bits and hence are termed narrow. In this paper, a parity generator circuit design is proposed that is capable of generating parity if the input value is narrow. It is shown that the parity can be generated faster than a regular XOR tree implementation using this design for the values that can be represented using fewer bits.

The proposed technique reduces the parity generation latency of 64‐bit values by 50 percent for eight‐bit narrow values. Considering the fact that around 70 percent of the immediate values written to the immediate field of the issue queue and around 40 percent of the value written to the integer register file can be expressed with only eight bits, the coverage of the proposed scheme is quite high.

This paper shows the simulation results of fast parity generator circuit if the input value is narrow.

This study aims to find the usefulness of the customized surgical osteotomy guide (CSOG) for accurate mandibular tumor resection for boosting the accuracy of prefabricated customized implant fixation in mandibular reconstructions.

In all, 30 diseased mandibular RP models (biomodels) were allocated for the study (for experimental group [n = 15] and for control group [n = 15]). To reconstruct the mandible with customized implant in the experimental group, CSOGs and in control group, no CSOG were used for accurate tumor resections. In control group, only preoperative virtual surgical planning (VSP) and reconstructed RP mandible model were used for the reference. Individually each patient’s preoperative mandibular reconstructions data of both the groups were superimposed to the preoperative VSP of respective patient by registering images with the non-surgical side of the mandible. In both the groups, 3D measurements were taken on the reconstructed side and compared the preoperative VSP and postoperative reconstructed mandible data. The sum of the differences between pre and postoperative data was considered as the total error. This procedure was followed for both the groups and compared the obtained error between the two groups using statistical analysis.

The use of CSOG for accurate tumor resection and exact implant fixation in mandibular reconstruction produced a smaller total error than without using CSOG.

The results showed that, benefits provided with the use of CSOG in mandibular reconstruction justified its use over the without using CSOG, even in free hand tumor resection using rotating burr.

The purpose of this study is to demonstrate and characterise a soft-tooled micro-injection moulding process through in-line measurements and surface metrology using a data-intensive approach.

A soft tool for a demonstrator product that mimics the main features of miniature components in medical devices and microsystem components has been designed and fabricated using material jetting technique. The soft tool was then integrated into a mould assembly on the micro-injection moulding machine, and mouldings were made. Sensor and data acquisition devices including thermal imaging and injection pressure sensing have been set up to collect data for each of the prototypes. Off-line dimensional characterisation of the parts and the soft tool have also been carried out to quantify the prototype quality and dimensional changes on the soft tool after the manufacturing cycles.

The data collection and analysis methods presented here enable the evaluation of the quality of the moulded parts in real-time from in-line measurements. Importantly, it is demonstrated that soft-tool surface temperature difference values can be used as reliable indicators for moulding quality. Reduction in the total volume of the soft-tool moulding cavity was detected and quantified up to 100 cycles. Data collected from in-line monitoring was also used for filling assessment of the soft-tool moulding cavity, providing about 90% accuracy in filling prediction with relatively modest sensors and monitoring technologies.

This work presents a data-intensive approach for the characterisation of soft-tooled micro-injection moulding processes for the first time. The overall results of this study show that the product-focussed data-rich approach presented here proved to be an essential and useful way of exploiting additive manufacturing technologies for soft-tooled rapid prototyping and new product introduction.