Change of the yield stress in roll formed ERW pipes considering the Bauschinger effect

ERW pipes formed with the roll forming process show a yield stress distribution along the circumferential direction and their quality is strongly influenced by the magnitude and by the distributions of the yield stress. In addition to that, strips are subjected to cyclic loading during roll forming process. Since ERW pipes are firstly roll formed, welded and then sized, in order to develop an enhanced predicting method for the calculation of the ERW pipe yield stress, the same process flow has been also applied to authors\u2019 numerical simulations. The Yoshida-Uemori kinematic hardening model has been applied considering several subdivision of the strain range, and different parameters, aiming to find the best correlation between the estimated Bauschinger effect and the one measured in the relevant cyclic loading experiment. The comparisons between estimated and experimentally-measured values of the thickness distribution, and of the locally-measured yield stress, prove both reliability and accuracy of the adopted process chain analysis. The growth of the sizing effect ratio has shown to cause the increase of the yield stress, which becomes more uniform along the circumferential direction

A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit

Hardware-based spiking neural networks (SNNs) to mimic biological neurons have been reported. However, conventional neuron circuits in SNNs have a large area and high power consumption. In this work, a split-gate floating-body positive feedback (PF) device with a charge trapping capability is proposed as a new neuron device that imitates the integrate-and-fire function. Because of the PF characteristic, the subthreshold swing (SS) of the device is less than 0.04 mV/dec. The super-steep SS of the device leads to a low energy consumption of ∼0.25 pJ/spike for a neuron circuit (PF neuron) with the PF device, which is ∼100 times smaller than that of a conventional neuron circuit. The charge storage properties of the device mimic the integrate function of biological neurons without a large membrane capacitor, reducing the PF neuron area by about 17 times compared to that of a conventional neuron. We demonstrate the successful operation of a dense multiple PF neuron system with reset and lateral inhibition using a common self-controller in a neuron layer through simulation. With the multiple PF neuron system and the synapse array, on-line unsupervised pattern learning and recognition are successfully performed to demonstrate the feasibility of our PF device in a neural network

Smart Contact Lenses with Graphene Coating for Electromagnetic Interference Shielding and Dehydration Protection

Recently, smart contact lenses with electronic circuits have been proposed for various sensor and display applications where the use of flexible and biologically stable electrode materials is essential. Graphene is an atomically thin carbon material with a two-dimensional hexagonal lattice that shows outstanding electrical and mechanical properties as well as excellent biocompatibility. In addition, graphene is capable of protecting eyes from electromagnectic (EM) waves that may cause eye diseases such as cataracts. Here, we report a graphene-based highly conducting contact lens platform that reduces the exposure to EM waves and dehydration. The sheet resistance of the graphene on the contact lens is as low as 593 Ω/sq (±9.3%), which persists in an wet environment. The EM wave shielding function of the graphene-coated contact lens was tested on egg whites exposed to strong EM waves inside a microwave oven. The results show that the EM energy is absorbed by graphene and dissipated in the form of thermal radiation so that the damage on the egg whites can be minimized. We also demonstrated the enhanced dehydration protection effect of the graphene-coated lens by monitoring the change in water evaporation rate from the vial capped with the contact lens. Thus, we believe that the graphene-coated contact lens would provide a healthcare and bionic platform for wearable technologies in the future

Review of candidate devices for neuromorphic applications

Artificial intelligence technology has attracted much attention in recent years, and technological progress of this technology is anticipated with the development of semiconductor technology. This talk focuses on synaptic mimic devices to realize artificial intelligence with semiconductor memory technology. These synaptic devices affect cognitive accuracy along with conductance quantization and architecture. Therefore, we will first discuss from the architectural point of view and examine the characteristics of candidates for various synapse devices being reported. In particular, we concentrate on synaptic imitation devices that creatively use the functions of several flash memory devices. Finally, we discuss device variation and IR drop along metal wires as common challenges for synaptic devices, and how neuromorphic technology will evolve.N