This page contains links to all of the web-based projects completed for ENME 808S for the Fall 1999 semester.  Please follow any of the links below to view any of the individual websites.

Bayesian Belief Networks - This site explains the advantages of using Bayesian Belief Networks (BBN), graphical networks that represents probabilistic relationships among variables, over using mathematical formulas and prose.  It also explains how to create a BBN and what the outputs of the BBN will be, as well as the limitations; an example using a BBN to assess reliability is given.

Built-In Tests (Chips And Boards): Cost Tradeoffs - This site explains die-level and board-level built-in test procedures in comparison to conventional test steps in the manufacturing process of ICs.  Tradeoffs are described in the areas of improving yield versus increasing cost.  An example is given to weigh these tradeoffs.

Confidence Level Estimation in Monte Carlo Analysis - Monte Carlo methods can be loosely described as statistical simulation methods, where statistical simulation is defined in quite general terms to be any method that utilizes sequences of random numbers to perform the simulation.  This site explains how Monte Carlo methods can be used in the estimations of the number of samples for a given confidence level for a process.

Cost as an Independent Variable and Holonic Planning - This site explains how CAIV is a consideration in design for cost, and the difference in design for cost (DFC) and design to cost (DTC).  It also describes Holonic Planning, a process that integrates the entire range of manufacturing activities from order booking through design, production, and marketing to realize the agile manufacturing enterprise

Decisions Under Uncertainty - This site describes five rules that are utilized in the industry when decisions must be made under uncertainty.  They are:

Hurwicz criterion; 
Laplace insufficient reason criterion; 
Maximax criterion; 
Maximin criterion; 
Savage minimax regret criterion.

Diagnosis - This site describes the steps in performing a proper fault diagnosis, how to judge the performance of the test involved in the diagnosis, as well as the problems, cost, and tools used in diagnois. It also explains several different methods of diagnosis including fuzzy logic and neural networks. A case study is also presented on the topic.

Hot Lots - This site describes "hot" lots, those lots that have been accelerated in the fabrication process due to some new priority in industry.  It also details the consequences of hot lots and their impact on the cycle time of other lots, fab performance, scheduling and yield.

Maintenance - This site describes both preventive and corrective maintenance, maintenance planning, estimating the cost of maintenance and equipment time breakdown.  Included is a sample maintenance calculator for estimating costs in a sample situation.

Monte Carlo Analysis Applied to Fault Coverage - This site gives background on Monte Carlo methods and shows how they may be used to estimate fault coverage when incorporated into the test steps of a process.  It also gives background on fault detection methods and differentiates between fault detection, diagnosis, and identification.

Net Present Value - This site explains how NPV reduces a stream of costs and benefits to a single number in which costs or benefits which are projected to occur in the future are "discounted."  It also describes how NPV may be used in the electronic parts industry.

Signature Analysis - This site describes the steps in performing a signature analysis and explains how signature analysis has become an effective method for organizing data allowing for the dynamic behavior of a wafer fabrication system to be studied.  Also, it shows the role signature analysis plays in evaluating the effect of dispatch schemes on the operation of a semiconductor factory.

Variation in Defect Density with Size Scaling - This site explains how size scaling may be used in conjunction with defect density to produce a better estimation of the yield and faults per chip due to the nature of the formation of defects on a wafer, based on the Poisson yield model.

Yield Learning - This site explains the importance of yield learning, the process of correcting yield loss as quickly as possible, in manufacturing processes, specifically VLSI processes, to reduce costs and maintain schedule of delivery.  It also discusses yield learning as applied to the areas of VLSI Fabrication and Simulation.

Yield Modeling with Redundancy - This site discusses how to estimate yield in systems including redundancy according to fault clustering distributions.  It also discusses how to approximate a variety of clustering modes according to several different distribution models, including gamma, triangular, rectangular, exponential and delta function distributions.