A millimeter wave (MMW) on-chip passive frequency-dependent inductor is described with different designed inductance values targeted at different frequency ranges using one device. The proposed frequency-dependent inductor design allows the optimization of multi-band impedance matching for MMW analog circuit designs with a single all-passive on-chip device. This device uses low-loss natural capacitors and multiple capacitively loaded ground return lines to provide MMW frequency-dependent effective device inductance. No on-chip switches are required in the proposed inductor design and it can allow circuit size reduction by allowing a single inductor to target different frequency ranges. Simulated inductance values in a 130 nm BiCMOS technology show a 61% change in inductance is possible between two frequency ranges: f <; 25 GHz, and f > 48 GHz. The proposed frequency-dependent inductor design has also been designed and measured in a 45 nm CMOS process. Measured results show that the use of multiple capacitively loaded ground return lines is effective in creating an on-chip MMW frequency-dependent inductor.

As a result of their adaptability, artificial neural networks present good solutions for a permanently increasing range of industrials problems. So, if their usefulness has already been confirmed, very few papers deal with real applications of this kind of technology. Our goal is to present a neural based solution that we have developed for visual inspection in VLSI production for the IBM Essonnes plant. The main characteristics of such systems are real-time control and high reliability in detection and classification tasks. The presented system is based on a ZISC©, an IBM hardware implementation of the Restricted Coulomb Energy algorithm and of the K-Nearest Neighbor algorithm. The goal of the developed application is to inspect vias for probe damage during wafer tests: each via is analyzed and classified (good impact, bad impact or absence of impact). First results are really encouraging and show the efficiency of this system in manufacturing environment.

The present article concerns neural based image processing and solutions developed for industrial problems using the ZISC-036 neuro-processor, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm (RCE) and the K-Nearest Neighbor algorithm (KNN). The developed neural based techniques have been applied for image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). We also have developed and implemented on ZISC-036 neuro-processor, a neural network based solution for visual probe mark inspection in VLSI production for the IBM Essonnes plant. The main characteristics of such systems are real-time control and high reliability in detection and classification tasks. Experimental results, validating presented concepts, have been reported showing quantitative and qualitative improvement as well as the efficiency our solutions.