Capacitor-Less Low-Power Neuron Circuit with Multi-Gate Feedback Field Effect Transistor

Recently, research on artificial neuron circuits imitating biological systems has been actively studied. The neuron circuit can implement an artificial neural network (ANN) capable of low-power parallel processing by configuring a biological neural network system in hardware. Conventional CMOS analog neuron circuits require many MOSFETs and membrane capacitors. Additionally, it has low energy efficiency in the first inverter stage connected to the capacitor. In this paper, we propose a low-power neuron circuit with a multi-gate feedback field effect transistor (FBFET) that can perform integration without a capacitor to solve the problem of an analog neuron circuit. The multi-gate FBFET has a low off-current due to its low operating voltage and excellent sub-threshold characteristics. We replace the n-channel MOSFET of the inverter with FBFET to suppress leakage current. FBFET devices and neuron circuits were analyzed using TACD and SPICE mixed-mode simulation. As a result, we found that the neuron circuit with multi-gate FBFET has a low subthreshold slope and can completely suppress energy consumption. We also verified the temporal and spatial integration of neuron circuits

DDR4 Data Channel Failure Due to DC Offset Caused by Intermittent Solder Ball Fracture in FBGA Package

This paper shows that an intermittent AC coupling defect occurring in a DDR4 data channel will cause more intermittent errors in DDR4, compared to such defect in DDR3. The intermittent AC coupling defect occurs due to intermittent fracture in DDR4 package solder ball. The defect causes DC offset in DDR4, which shifts the data signal or data eye and results in DDR4 data channel failure. The DC offset occurs due to the asymmetric nature of pseudo open drain termination scheme. DDR4 data channel response is compared with DDR3 channel. It is shown that pseudo random binary sequence (PRBS) pattern will always cause failure for DDR4, but PRBS will only cause failure in DDR3 if the sequence of consecutive 0's or 1's in PRBS pattern is long enough to cause threshold violation. As a result there will be more intermittent errors in DDR4 compared to DDR3. The defect due to fracture in solder ball is modelled by an AC coupling capacitor. A 1nF AC coupling capacitor corresponding to a solder ball fracture of height about 1nm is used to show the difference between DDR4 and DDR3 response

Temperature Estimation of HBM2 Channels with Tail Distribution of Retention Errors in FPGA-HBM2 Platform

High-bandwidth memory 2 (HBM2) vertically stacks multiple dynamic random-access memory (DRAM) dies to achieve a small form factor and high capacity. However, it is difficult to diagnose HBM2 issues owing to their structural complexity and 2.5D integration with heterogeneous chips. The effects of the temperature at the base logic die (TL), and the refresh interval at the stacked DRAM dies, were experimentally investigated by counting the dynamic retention errors in the eight channels in an HBM2. TL was indirectly controlled by the heatsink temperature (TS). The lognormal distribution represents the distribution of the cell counts with varying refresh times. All Z-magnitudes (multiples of the distribution standard deviation) over the various refresh cycle times (RCTs) up to 2.045 s in a single channel at TL of 70 °C appeared below 4.4, which means that the error bits belong to the tail distribution. The Z-differences in the eight channels were distinctively larger than the Z-differences of the same channels at a constant temperature, demonstrating that the temperature difference in the stacked dies resulted in larger Z-differences. The largest Z-difference was 0.091 for all the channels at an RCT of 1.406 s, which was approximately 4.82 times smaller than the Z-difference between the TL temperatures of 70 °C and 80 °C in a single channel. The Z-difference between the TL temperatures of 70 °C and 72 °C in a single channel was approximately the same as the Z-difference in all the channels at an RCT of 2.045 s

Demonstration of Two-Dimensional Beam Steering through Wavelength Tuning with One-Dimensional Silicon Optical Phased Array

We demonstrate two-dimensional beam steering through wavelength control using a one-dimensional optical phased array (OPA) in which a path difference is built up in each channel to allocate a phase delay sequentially. Prior to the beam steering through wavelength tuning, phase initialization was performed to form a single beam using electro-optic p-i-n phase shifters to compensate for the phase error due to fabrication imperfections. With a 79.6 μm path difference in the phase-feeding lines and a 2 μm pitch in the grating radiators, we achieved a continuous transversal steering of about 46° through a wavelength tuning of about 7 nm. By extending the wavelength tuning range to 90 nm, longitudinal steering was attained near 13° with a discrete interval of about 1°. The beam was maintained during full two-dimensional steering and experienced only a small degree of degradation in the beam divergences and in the side lobe level. We analyzed the parameters to be able to induce the degradation of beam quality considering the fabrication errors of the geometric parameters of the OPA. The results indicated that the scanning scheme employing wavelength tuning after initialization with phase shifters can greatly reduce the realignment process of the beam pattern, even in the presence of some effective index perturbation during the fabrication

Recognition of Assembly Instructions Based on Geometric Feature and Text Recognition

Recent advances in machine learning methods have increased the performances of object detection and recognition systems. Accordingly, automatic understanding of assembly instructions in manuals in the form of electronic or paper materials has also become an issue in the research community. This task is quite challenging because it requires the automatic optical character recognition (OCR) and also the understanding of various mechanical parts and diverse assembly illustrations that are sometimes difficult to understand even for humans. Although deep networks are showing high performance in many computer vision tasks, it is still difficult to perform this task by an end-to-end deep neural network due to the lack of training data, and also because of diversity and ambiguity of illustrative instructions. Hence, in this paper, we propose to tackle this problem by using both conventional non-learning approaches and deep neural networks, considering the current state-of-the-arts. Precisely, we first extract components having strict geometric structures, such as characters and illustrations, by conventional non-learning algorithms, and then apply deep neural networks to recognize the extracted components. The main targets considered in this paper are the types and the numbers of connectors, and behavioral indicators such as circles, rectangles, and arrows for each cut in do-it-yourself (DIY) furniture assembly manuals. For these limited targets, we train a deep neural network to recognize them with high precision. Experiments show that our method works robustly in various types of furniture assembly instructions.N

Dimension-controlled synthesis of CdS nanocrystals: From 0D quantum dots to 2D nanoplates

The dimension-controlled synthesis of CdS nanocrystals in the strong quantum confinement regime is reported. Zero-, one-, and two-dimensional CdS nanocrystals are selectively synthesized via low-temperature reactions using alkylamines as surface-capping ligands. The shape of the nanocrystals is controlled systematically by using different amines and reaction conditions. The 2D nanoplates have a uniform thickness as low as 1.2 nm. Furthermore, their optical absorption and emission spectra show very narrow peaks indicating extremely uniform thickness. It is demonstrated that 2D nanoplates are generated by 2D assembly of CdS magic-sized clusters formed at the nucleation stage, and subsequent attachment of the clusters. The stability of magic-sized clusters in amine solvent strongly influences the final shapes of the nanocrystals. The thickness of the nanoplates increases in a stepwise manner while retaining their uniformity, similar to the growth behavior of inorganic clusters. The 2D CdS nanoplates are a new type of quantum well with novel nanoscale properties in the strong quantum confinement regime.