High density and high speed nonvolatile random access memory is described. Using the conventional floating gate EEPROM process, a dynamic RAM cell has been merged into an EEPROM cell. Data stored on the DRAM cell can be backed up by the EEPROM cell. The data transfer between the DRAM and the EEPROM is executed simultaneously on all memory cells on a same word line. An experimental 16 kbit memory has been manufactured by 1.5 µm design rule CMOS process. The cell size is 17 µm17 µm and the chip size is 57.2 mm3.75 mm. The address access time of 100 ns and the page read access time of 20 ns have been achieved. A nonvolatile CAM (Content Addressable Memory) cell will also be proposed.

A memory array architecture and row decoding scheme for a 3 V only DINOR (divided bit line NOR) flash memory has been designed. A new sector organization realizes one word line driver per two word lines, which is conformable to tight word line pitch. A hierarchical negative voltage switching row decoder and a compact source line driver have been developed for 1 K byte sector erase without increasing the chip size. A bit-by-bit programming control and a low threshold voltage detection circuit provide a high speed random access time at low Vcc and a narrow program threshold voltage distribution. A 4 Mb DINOR flash memory test device was fabricated from 0.5 µm, double-layer metal, triple polysilicon, triple well CMOS process. The cell measures 1.81.6 µm2 and the chip measures 5.85.0 mm2. The divided bit line structure realizes a small NOR type memory cell.

This paper presents the Received-Signal-Strength-Indicator (RSSI) based living-body radar, which uses only a single RF front-end and a few parasitic antennas. This radar measures the RSSI variation at the single active antenna while varying the terminations of the parasitic antennas. The propagation channel is estimated from just the temporal transition of RSSI; our proposal reconstructs the phase information of the signal. In this paper, we aim to estimate the direction of living-body. Experiments are carried out and it is found that most angular errors are within the limit of the angular width of the living-body.

This paper proposes a method for cooperative multi-static Multiple Input Multiple Output (MIMO) radar that can estimate the number of targets. The purpose of this system is to monitor humans in an indoor environment. First, target positions within the estimation range are roughly detected by the Capon method and the mode vector corresponding to the detected positions is calculated. The mode vector is multiplied by the eigenvector to eliminate the virtual image. The spectrum of the evaluation function is calculated from the remaining positions, and the number of peaks in the spectrum is defined as the number of targets. Experiments carried out in an indoor environment confirm that the proposed method can estimate the number of targets with high accuracy.

This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).

To improve the performance of high-density flash memories, several circuit technologies have been developed. A word-line boost and clamp scheme realizes low supply voltage read operations. A flash programming scheme utilizing Fowler-Nordheim (F-N) tunneling for programming before erasure and a negative gate biased erasing scheme accomplish low-power, high-speed, and 5-V-only erase operations. The chip size penalty is estimated to be only 3% for the 16-Mb flash memories.

This paper describes a neuro-based optimization algorithm for three dimensional (3-D) cylindric puzzles which are problems to arrange the irregular-shaped slices so that they perfectly fit into a fixed three dimensional cylindric shape. First, the idea to expand the 2-dimensional tiling technique to 3-dimensional puzzles is described. Next, to energy function with the fitting function of each polyomino is introduced, which is available for 3-D cylindric puzzles. Furthermore our algorithm is applied to several examples using the analog neural array. Finally, it is shown that our algorithm is useful for solving 3-D cylindric puzzles.