A data analysis technology of atomic force microscopy for atomically flat silicon surfaces has been developed. Atomically flat silicon surfaces composed of atomic terraces and steps are obtained on (100) orientation 200 mm diameter wafers by annealing in pure argon ambience at 1,200 for 30 minutes. Atomically flat silicon surfaces are lead to improve the MOS inversion layer mobility and current drivability of MOSFETs and to decrease the fluctuations in electrical characteristics of MOSFETs. It is important to realize the technology that evaluates the flatness and the uniformity of atomically flat silicon surfaces. The off direction angle is calculated by using two straight edge lines selected from measurement data. And the off angle is calculated from average atomic terrace width under assumption that height difference between neighboring terraces is equal to the step height, 0.135 nm, of (100) silicon surface. The analyzing of flatness of each terrace can be realized by converting the measurement data using the off direction angle and the off angle. And, the average roughness of each terrace is about 0.017-0.023 nm. Therefore, the roughness and the uniformity of each terrace can be evaluated by this proposed technique.

A new vector-matching circuit technology has been developed aiming at compact implementation of maximum likelihood search engine for neuron-MOS associative processor. The new matching cell developed in this work possessed the template information in the form of an analog mask ROM and calculates the absolute value of difference between the template vector and the input vector components. The analog-mask ROM merged matching cell is composed of only five transistors to be compared with our earlier-version memory separated matching cell of 13 transistors. In addition, the undesirable cell-to-cell data interference through the common floating node ("boot-strap effect") has been eliminated without using power-consuming current source loads in source followers. As a result, dc-current-free matching cell operation has been established, making it possible to build a low-power, high-density search engine. Test circuits were fabricated by a 0.8-µm double-polysilicon double-metal n-well CMOS process, and the circuit operation has been experimentally verified.

An analog vector quantization processor has been designed based on the neuron-MOS (νMOS) technology. In order to achieve a high integrating density, template information is merged into the matching cell (the absolute value circuitry) using the νMOS ROM technology. A new-architecture νMOS winner-take-all (WTA) circuit is employed for fully-parallel search for the minimum-distance vector. The WTA performs multi-resolution winner search with an automatic feedback gain control. A test chip having 256 16-element fixed template vectors has been built in a 1.5-µm double-polysilicon CMOS technology with the chip size of 7.2 mm 7.2 mm, and the basic operation of the circuits has been demonstrated.