A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Optical contrast in ion-implanted amorphous silicon carbide nanostructures

Topographic and optical contrasts formed by Ga+ ion irradiation of thin films of amorphous silicon carbide have been investigated with scanning near-field optical microscopy. The influence of ion-irradiation dose has been studied in a pattern of sub-micrometre stripes. While the film thickness decreases monotonically with ion dose, the optical contrast rapidly increases to a maximum value and then decreases gradually. The results are discussed in terms of the competition between the effects of ion implantation and surface milling by the ion beam. The observed effects are important for uses of amorphous silicon carbide thin films as permanent archives in optical data storage applications.

Design, Manufacture and Performance of Germanium Bipolar Transistors

Germanium NPN bipolar transistors have been manufactured using phosphorus and boron ion implantation processes. Implantation and subsequent activation processes have been investigated for both dopants. Full activation of phosphorus implants has been achieved with RTA schedules at 535?C without significant junction diffusion. However, boron implant activation was limited and diffusion from a polysilicon source was not practical for base contact formation. Transistors with good output characteristics were achieved with an Early voltage of 55V and common emitter current gain of 30. Both Silvaco process and device simulation tools have been successfully adapted to model the Ge BJT(bipolar junction transistor) performance.

Hard coatings on elastomers for reduced permeability and increased wear resistance

In this study, three different elastomers, namely hydrogenated nitrile butadiene rubber, fluoroelastomer and silicone, have been subjected to two different hard metallised coatings by ion implantation process. The three different elastomers are commonly used in various seal applications, where reduced wear and gas permeability are essential in maintaining seal performance and functionality. Samples of these rubbers have been coated with chromium coating in one set of tests. In the second set of tests, samples of elastomers have been coated with tungsten carbide coating being deposited on all the three different elastomers. Wear, gas permeability and mechanical behaviour of the coated samples were compared with each other and with the control uncoated elastomers. All the coated samples showed good reduction in gas permeability. With the use of metallised coatings, there has been improved resistance to wear in all the coated samples. Adhesion strength and effect of coating on the elastomer have been investigated by mechanical testing. Mechanical tests revealed good adhesion of metal coatings on all the rubber samples, and there was no detrimental effect on the mechanical properties after coating. © 2012 Institute of Materials, Minerals and Mining.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Creation of damage-free ferroelectric nanocapacitors via focused ion beam milling

We present a novel method for creating damage-free ferroelectric nanostructures with a focused ion beam milling machine. Using a standard e-beam photoresist followed by a dilute acid wash, nanostructures ranging in size from 1 mu m down to 250 nm were created in a 90 nm thick lead zirconate titanate ( PZT) wafer. Transmission electron microscopy and piezoresponse force microscopy ( PFM) confirmed that the surfaces of the nanostructures remained damage free during fabrication, and showed no gallium implantation, and that there was no degradation of ferroelectric properties. In fact DC strain loops, obtained using PFM, demonstrated that the nanostructures have a higher piezoresponse than unmilled films. As the samples did not have any top hard mask, the method presented is unique as it allows for imaging of the top surface to understand edge effects in well-defined nanostructures. In addition, as no post-mill annealing was necessary, it facilitates investigation of nanoscale domain mechanisms without process-induced artefacts.