R/electromigration.r
In KDB2/amsReliability: ams AG Reliability Analysis Package
Defines functions
AddArea
ReadDataAce
BlackModelization
BlackAnalysis
BlackModelization.me
AdjustCensor
Documented in
BlackAnalysis
################################################################################
### ###
### INFORMATIONS ###
### --------------------------------- ###
### ###
### PACKAGE NAME amsReliability ###
### MODULE NAME electromigration.r ###
### VERSION 0.10 ###
### ###
### AUTHOR Emmanuel Chery ###
### MAIL emmanuel.chery@ams.com ###
### DATE 2016/02/24 ###
### PLATFORM Windows 7 & Gnu/Linux 3.16 ###
### R VERSION R 3.1.1 ###
### REQUIRED PACKAGES ggplot2, grid, MASS, nlstools, scales ###
### LICENSE GNU GENERAL PUBLIC LICENSE ###
### Version 3, 29 June 2007 ###
### ###
### ###
### DESCRIPTION ###
### --------------------------------- ###
### ###
### This package is a collection of scripts dedicated to help ###
### the process reliability team of ams AG. It includes tools to ###
### quickly visualize data and extract model parameters in order ###
### to predict device lifetimes. ###
### ###
### This module is dedicated to electromigration experiments. ###
### Extraction of Black's parameters is performed. ###
### ###
### ###
### FUNCTIONS ###
### --------------------------------- ###
### ###
### AddArea Retrieve structure physical dimensions ###
### BlackAnalysis Main function for data analysis ###
### BlackModelization Extraction of Black's parameters ###
### ReadDataAce Read Exportfile and create data table ###
### ###
################################################################################
AddArea <- function(DataTable, DeviceID)
# Retrive the area of an EM device and add it to the dataTable
# Return the new dataTable if it succeeds.
# Return an error otherwise
{
# Create an error to return in case of need
errorMsg <- try(log("a"), silent=TRUE)
# Read the list of device to retrieve the section parameters.
ListDevice <- try(read.delim("//fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt"),silent=TRUE)
#if file is not present, error is returned.
if (class(ListDevice) == "try-error"){
print("File //fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt not found.")
return(errorMsg)
} else {
W <- ListDevice$Width[ListDevice$Device==DeviceID] # micrometers
H <- ListDevice$Height[ListDevice$Device==DeviceID] # micrometers
Area <- W*H*1E-12 # m^2
# if Area is a positive number different from 0, we can proceed:
if (is.na(Area) || Area <=0 || length(Area)==0) {
print(paste("Structure",DeviceID, "is not present in the list. Please fill the list!"))
# Force an error in the return for BlackAnalysis.
return(errorMsg)
} else { # we proceed
DataTable$Area <- Area
return(DataTable)
}
}
}
ReadDataAce <- function(ListFileName, StructureList=c())
# Read the exportfiles listed in ListFileName and store them in a dataframe.
# First read all the files and then calculate the probability scale
# for each condition. This allows to work with conditions splitted in different files.
# Data are cleaned to remove bad units
# Exportfile from Ace and Mira have different headers,
# therefore column numbers are used
{
# ResTable initialisation
ResTable <- data.frame()
for (file in ListFileName){
# Read the file and store it in a temporary dataframe
TempTable <- read.delim(file)
# Creation of the new dataframe
TTF <- TempTable[,3]
Status <- TempTable[,2]
Stress <- TempTable[,5]
Temperature <- TempTable[,8]
Condition <- paste(TempTable[,5],"mA/",TempTable[,8],"°C",sep="") #paste(TempTable[,"Istress"],"mA/",TempTable[,"Temp"],"°C",sep="")
Dimension <- TempTable[,6]
# Creation of a dataframe to store the data
TempDataFrame <- data.frame(TTF,Status,Condition,Stress,Temperature,Dimension)
# Force the column names
names(TempDataFrame) <- c("TTF", "Status", "Conditions", "Stress", "Temperature","Dimension")
# Store the data in the final table
ResTable <- rbind(ResTable,TempDataFrame)
}
# security check, we force again the name on ResTable
names(ResTable) <- c("TTF", "Status", "Conditions", "Stress", "Temperature", "Dimension")
# Cleaning to remove units where status is not 1 or 0.
ResTable <- Clean(ResTable)
# If Structure is not empty, we select only the structures listed.
if (length(StructureList) != 0){
NewResTable <- data.frame()
for (strucLength in StructureList){
NewResTable <- rbind(NewResTable,ResTable[ResTable$Dimension==strucLength,])
}
ResTable <- NewResTable
}
# Handle case where some unfailed samples have a lower TTF than finished ones.
ResTable <- AdjustCensor(ResTable)
# List the conditions present in ResTable
CondList <- levels(factor(ResTable$Conditions))
# Probability is missing. Let's add it.
# Final dataframe is ExpDataTable
# Initialisation
ExpDataTable <- data.frame()
# For each condition found, we calculate the probability of failure. Data are stacked in ExpDataFrame. Lognormal scale is used.
for (cond in CondList){
TempDataTable <- CreateDataFrame(ResTable$TTF[ResTable$Conditions==cond], ResTable$Status[ResTable$Conditions==cond],
ResTable$Condition[ResTable$Conditions==cond], ResTable$Stress[ResTable$Conditions==cond], ResTable$Temperature[ResTable$Conditions==cond], Scale="Lognormal",ResTable$Dimension[ResTable$Conditions==cond])
ExpDataTable <- rbind(ExpDataTable,TempDataTable)
}
# We force the new names here as a security check.
names(ExpDataTable) <- c("TTF", "Status", "Probability", "Conditions", "Stress", "Temperature","Dimension")
# Order the condition in numerical/alphabetical order
ExpDataTable <- ExpDataTable[OrderConditions(ExpDataTable),]
# Do the same for conditions levels
ExpDataTable$Conditions <- factor(ExpDataTable$Conditions, SortConditions(levels(ExpDataTable$Conditions)))
return(ExpDataTable)
}
BlackModelization <- function(DataTable, DeviceID)
# Modelize the data using Black equation
# Extract the parameters: A, n and Ea
# as well as the lognormal slope
# TTF = A j^(-n) exp(Ea/kT + Scale * Proba)
# Proba in standard deviations
# Data(TTF,Status,Probability,Conditions,Stress,Temperature, Dimension, Area)
# Data have to be cleaned upfront. Only valid data (status==1) should be given.
{
# Modelization
Model <- ModelFit(DataTable, Law="BlackLaw")
# Parameters Extraction
# A <- coef(Model)[1]
# n <- coef(Model)[2]
# Ea <-coef(Model)[3]
# Scale <- coef(Model)[4]
# Using the parameters and the conditions, theoretical distributions are created
ListConditions <- levels(DataTable$Conditions)
Area <- DataTable$Area[1]
ModelDataTable <- CreateModelDataTable(Model, ListConditions, Area, Law="BlackLaw", Scale="Lognormal")
# Display a few information regarding the model: parameters, goodness of fit...
FitResultsDisplay(Model, DataTable, DeviceID)
return(ModelDataTable)
}
#' Electromigration data analysis
#'
#' Extract Black's parameters from a set of electromigration experiments.
#' The experimental data as well as the resulting model are displayed and
#' can be saved. Extracted parameters are saved in a fit.txt file.
#'
#' @param ErrorBand displays the confidence intervals if set to TRUE.
#' @param ConfidenceValue percentage used in the confidence interval calculation
#' @param Save saves the chart as .png if set to TRUE.
#'
#' @return None
#'
#' @examples
#' BlackAnalysis()
#' BlackAnalysis(ErrorBand=FALSE)
#' @author Emmanuel Chery, \email{emmanuel.chery@@ams.com}
#' @import ggplot2 MASS scales nlstools tcltk
#' @export
BlackAnalysis <- function(ErrorBand=FALSE, ConfidenceValue=0.95, Save=TRUE)
{
# Disable warning for this function.
oldw <- getOption("warn")
options(warn = -1)
#rm(list=ls())
# ListFiles <- list.files(pattern="*exportfile.txt")
# Filters for file selection
Filters <- matrix(c("All files", "*", "Text", ".txt", "Export Files", "*exportfile.txt"),3, 2, byrow = TRUE)
ListFiles <- SelectFilesAdvanced(Filters)
# case 1, there are one or several files available
if (length(ListFiles) != 0){
# List of DeviceID available in the selected exportfiles
DeviceIDList <- levels(sapply(ListFiles,function(x){factor(strsplit(x,split="_")[[1]][2])}))
for (DeviceID in DeviceIDList){
SubListFiles <- ListFiles[grep(DeviceID,ListFiles)]
# Import the file(s) and create the 3 dataframes + display data
DataTable <- try(ReadDataAce(SubListFiles), silent=TRUE)
if (class(DataTable) != "try-error"){
# Reading the file was ok.
# Try to import the area
if (class(AddArea(DataTable, DeviceID)) != "try-error" ){
DataTable <- AddArea(DataTable, DeviceID)
}
# Modelization, errorBands calculation and Graph is made with a clean table where only failed samples are kept.
# DataTable is kept in order to be saved in fit.txt
CleanExpDataTable <- KeepOnlyFailed(DataTable)
# Attempt to modelize. If succes, we plot the chart, otherwise we only plot the data.
ModelDataTable <- try(BlackModelization(CleanExpDataTable, DeviceID),silent=TRUE)
if (class(ModelDataTable) != "try-error"){
if (ErrorBand){
ErrorDataTable <- ErrorEstimation(CleanExpDataTable, ModelDataTable, ConfidenceValue)
} else {
ErrorDataTable <- NULL
}
CreateGraph(CleanExpDataTable, ModelDataTable, ErrorDataTable, aesVec = c("TTF", "Probability", "Conditions"), title = DeviceID,
axisTitles = c("Time to Failure (s)","Probability (%)"), scale.x = "Log", scale.y = "Lognormal", save = Save )
# ExpData are added to the fit.txt file created during modelization
SaveData2File(DataTable, "fit.txt")
# There was an error either with Area or with modelization, we go to fallback mode
} else { # if modelization is not a success, we display the data and return parameters of the distribution in the console (scale and loc) in case user need them.
ModelDataTable <- FitDistribution(CleanExpDataTable,Scale="Lognormal")
CreateGraph(CleanExpDataTable, ModelDataTable, aesVec = c("TTF", "Probability", "Conditions"), title = DeviceID,
axisTitles = c("Time to Failure (s)","Probability (%)"), scale.x = "Log", scale.y = "Lognormal", save = FALSE)
}
} else { # reading the files returned an error.
print("Error detected in the file(s) you selected. Please check your selection.")
}
}
} else { # case 2, there are no files available
print("You need to create the export files first!")
}
# return(DataTable)
# Warning are set on again.
options(warn = oldw)
}
BlackModelization.me <- function(DataTable, DeviceID)
# Modelize the data using Black equation
# Extract the parameters: A, n and Ea
# as well as the lognormal slope
# TTF = A j^(-n) exp(Ea/kT + Scale * Proba)
# Proba in standard deviations
# Data(TTF,Status,Probability,Conditions,Stress,Temperature, Dimension)
{
# Read the list of device to retrieve the section parameters.
ListDevice <- try(read.delim("//fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt"),silent=TRUE)
#if file is not present, error is returned.
if (class(ListDevice) == "try-error"){
print("File //fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt not found.")
return(ListDevice)
} else {
W <- ListDevice$Width[ListDevice$Device==DeviceID] # micrometers
H <- ListDevice$Height[ListDevice$Device==DeviceID] # micrometers
S <- W*H*1E-12 # m^2
# if S is a positive number different from 0, we can proceed:
if (is.na(S) || S<=0 ) {
print(paste("Structure",DeviceID, "is not present in the list. Please fill the list!"))
# Force an error in the return for BlackAnalysis.
ModelDataTable <- data.frame()
as(ModelDataTable,"try-error")
return(ModelDataTable)
} else { # we proceed
# Physical constants
k <- 1.38E-23 # Boltzmann
e <- 1.6E-19 # electron charge
# Remove the units where status is 0
CleanDataTable <- DataTable[DataTable$Status==1,]
B.f <- deriv(~log(exp(A)*(Stress*1E-3/1)^(-n)*exp((Ea*1.6E-19)/(1.38E-23*(Temperature+273.15))+Scale*Probability))/log(10),namevec = c('A','Ea','n','Scale'), function.arg = c('A','Ea','n','Scale','Stress', 'Temperature','Probability'))
y = log10(DataTable$TTF)
fit.nlmer <- nlmer(log10(y) ~ B.f(A,Ea,n,Scale,Stress, Temperature,Probability) ~ Temperature | Conditions, start=list(nlpars=c(A=30,Ea=0.7,n=2,Scale=0.3)), data=DataTable)
summary(fit.nlmer)
# Parameters Extraction
A <- coef(Model)[1]
n <- coef(Model)[2]
Ea <-coef(Model)[3]
Scale <- coef(Model)[4]
# Residual Sum of Squares
RSS <- sum(resid(Model)^2)
# Total Sum of Squares: TSS <- sum((TTF - mean(TTF))^2))
TSS <- sum(sapply(split(CleanDataTable[,1],CleanDataTable$Conditions),function(x) sum((x-mean(x))^2)))
Rsq <- 1-RSS/TSS # R-squared measure
#print(paste("Size on 150 rows:", format(object.size(Model), unit="Mb")))
# Using the parameters and the conditions, theoretical distributions are created
ListConditions <- levels(CleanDataTable$Conditions)
# Initialisation
ModelDataTable <- data.frame()
# y axis points are calculated. (limits 0.01% -- 99.99%) Necessary to have nice confidence bands.
Proba <- seq(qnorm(0.0001),qnorm(0.9999),0.05)
for (i in seq_along(ListConditions)){
# Experimental conditions:
Condition <- ListConditions[i]
I <- CleanDataTable$Stress[CleanDataTable$Conditions==Condition][1]
Temp <- CleanDataTable$Temperature[CleanDataTable$Conditions==Condition][1] # °C
# TTF calculation
TTF <- exp(A)*(I*0.001/S)^(-n)*exp((Ea*e)/(k*(273.15+Temp))+ Proba * Scale)
# Dataframe creation
ModelDataTable <- rbind(ModelDataTable, data.frame('TTF'=TTF,'Status'=1,'Probability'=Proba,'Conditions'=Condition,'Stress'=I,'Temperature'=Temp))
}
# Drawing of the residual plots
plot(nlsResiduals(Model))
# Display of fit results
print(DeviceID)
print(summary(Model))
print(paste("Residual squared sum: ",RSS,sep=""))
#print(coef(Model))
#print(sd(resid(Model)))
# Save in a file
capture.output(summary(Model),file="fit.txt")
cat("Residual Squared sum:\t",file="fit.txt",append=TRUE)
cat(RSS,file="fit.txt",append=TRUE)
cat("\n \n",file="fit.txt",append=TRUE)
cat("Experimental Data:",file="fit.txt",append=TRUE)
cat("\n",file="fit.txt",append=TRUE)
capture.output(DataTable,file="fit.txt",append=TRUE)
return(ModelDataTable)
}
}
}
AdjustCensor <- function(DataTable)
# Adjust the censoring level
# Handle case where some unfailed samples have a lower TTF than finished ones.
# Return the DataTable with adjusted status
# DataTable(TTF,Status,Probability,Conditions,Stress,Temperature, Dimension)
{
# List of available conditions
listConditions <- levels(as.factor(DataTable$Conditions))
for (cond in listConditions){
minTimeOngoingSample <- min(DataTable$TTF[DataTable$Status==0 & DataTable$Conditions == cond])
DataTable$Status[DataTable$Status==1 & DataTable$TTF > minTimeOngoingSample & DataTable$Conditions == cond] <- 0
}
return(DataTable)
}
KDB2/amsReliability documentation built on May 7, 2019, 12:05 p.m.
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Defines functions AddArea ReadDataAce BlackModelization BlackAnalysis BlackModelization.me AdjustCensor
Documented in BlackAnalysis
################################################################################
### ###
### INFORMATIONS ###
### --------------------------------- ###
### ###
### PACKAGE NAME amsReliability ###
### MODULE NAME electromigration.r ###
### VERSION 0.10 ###
### ###
### AUTHOR Emmanuel Chery ###
### MAIL emmanuel.chery@ams.com ###
### DATE 2016/02/24 ###
### PLATFORM Windows 7 & Gnu/Linux 3.16 ###
### R VERSION R 3.1.1 ###
### REQUIRED PACKAGES ggplot2, grid, MASS, nlstools, scales ###
### LICENSE GNU GENERAL PUBLIC LICENSE ###
### Version 3, 29 June 2007 ###
### ###
### ###
### DESCRIPTION ###
### --------------------------------- ###
### ###
### This package is a collection of scripts dedicated to help ###
### the process reliability team of ams AG. It includes tools to ###
### quickly visualize data and extract model parameters in order ###
### to predict device lifetimes. ###
### ###
### This module is dedicated to electromigration experiments. ###
### Extraction of Black's parameters is performed. ###
### ###
### ###
### FUNCTIONS ###
### --------------------------------- ###
### ###
### AddArea Retrieve structure physical dimensions ###
### BlackAnalysis Main function for data analysis ###
### BlackModelization Extraction of Black's parameters ###
### ReadDataAce Read Exportfile and create data table ###
### ###
################################################################################
AddArea <- function(DataTable, DeviceID)
# Retrive the area of an EM device and add it to the dataTable
# Return the new dataTable if it succeeds.
# Return an error otherwise
{
# Create an error to return in case of need
errorMsg <- try(log("a"), silent=TRUE)
# Read the list of device to retrieve the section parameters.
ListDevice <- try(read.delim("//fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt"),silent=TRUE)
#if file is not present, error is returned.
if (class(ListDevice) == "try-error"){
print("File //fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt not found.")
return(errorMsg)
} else {
W <- ListDevice$Width[ListDevice$Device==DeviceID] # micrometers
H <- ListDevice$Height[ListDevice$Device==DeviceID] # micrometers
Area <- W*H*1E-12 # m^2
# if Area is a positive number different from 0, we can proceed:
if (is.na(Area) || Area <=0 || length(Area)==0) {
print(paste("Structure",DeviceID, "is not present in the list. Please fill the list!"))
# Force an error in the return for BlackAnalysis.
return(errorMsg)
} else { # we proceed
DataTable$Area <- Area
return(DataTable)
}
}
}
ReadDataAce <- function(ListFileName, StructureList=c())
# Read the exportfiles listed in ListFileName and store them in a dataframe.
# First read all the files and then calculate the probability scale
# for each condition. This allows to work with conditions splitted in different files.
# Data are cleaned to remove bad units
# Exportfile from Ace and Mira have different headers,
# therefore column numbers are used
{
# ResTable initialisation
ResTable <- data.frame()
for (file in ListFileName){
# Read the file and store it in a temporary dataframe
TempTable <- read.delim(file)
# Creation of the new dataframe
TTF <- TempTable[,3]
Status <- TempTable[,2]
Stress <- TempTable[,5]
Temperature <- TempTable[,8]
Condition <- paste(TempTable[,5],"mA/",TempTable[,8],"°C",sep="") #paste(TempTable[,"Istress"],"mA/",TempTable[,"Temp"],"°C",sep="")
Dimension <- TempTable[,6]
# Creation of a dataframe to store the data
TempDataFrame <- data.frame(TTF,Status,Condition,Stress,Temperature,Dimension)
# Force the column names
names(TempDataFrame) <- c("TTF", "Status", "Conditions", "Stress", "Temperature","Dimension")
# Store the data in the final table
ResTable <- rbind(ResTable,TempDataFrame)
}
# security check, we force again the name on ResTable
names(ResTable) <- c("TTF", "Status", "Conditions", "Stress", "Temperature", "Dimension")
# Cleaning to remove units where status is not 1 or 0.
ResTable <- Clean(ResTable)
# If Structure is not empty, we select only the structures listed.
if (length(StructureList) != 0){
NewResTable <- data.frame()
for (strucLength in StructureList){
NewResTable <- rbind(NewResTable,ResTable[ResTable$Dimension==strucLength,])
}
ResTable <- NewResTable
}
# Handle case where some unfailed samples have a lower TTF than finished ones.
ResTable <- AdjustCensor(ResTable)
# List the conditions present in ResTable
CondList <- levels(factor(ResTable$Conditions))
# Probability is missing. Let's add it.
# Final dataframe is ExpDataTable
# Initialisation
ExpDataTable <- data.frame()
# For each condition found, we calculate the probability of failure. Data are stacked in ExpDataFrame. Lognormal scale is used.
for (cond in CondList){
TempDataTable <- CreateDataFrame(ResTable$TTF[ResTable$Conditions==cond], ResTable$Status[ResTable$Conditions==cond],
ResTable$Condition[ResTable$Conditions==cond], ResTable$Stress[ResTable$Conditions==cond], ResTable$Temperature[ResTable$Conditions==cond], Scale="Lognormal",ResTable$Dimension[ResTable$Conditions==cond])
ExpDataTable <- rbind(ExpDataTable,TempDataTable)
}
# We force the new names here as a security check.
names(ExpDataTable) <- c("TTF", "Status", "Probability", "Conditions", "Stress", "Temperature","Dimension")
# Order the condition in numerical/alphabetical order
ExpDataTable <- ExpDataTable[OrderConditions(ExpDataTable),]
# Do the same for conditions levels
ExpDataTable$Conditions <- factor(ExpDataTable$Conditions, SortConditions(levels(ExpDataTable$Conditions)))
return(ExpDataTable)
}
BlackModelization <- function(DataTable, DeviceID)
# Modelize the data using Black equation
# Extract the parameters: A, n and Ea
# as well as the lognormal slope
# TTF = A j^(-n) exp(Ea/kT + Scale * Proba)
# Proba in standard deviations
# Data(TTF,Status,Probability,Conditions,Stress,Temperature, Dimension, Area)
# Data have to be cleaned upfront. Only valid data (status==1) should be given.
{
# Modelization
Model <- ModelFit(DataTable, Law="BlackLaw")
# Parameters Extraction
# A <- coef(Model)[1]
# n <- coef(Model)[2]
# Ea <-coef(Model)[3]
# Scale <- coef(Model)[4]
# Using the parameters and the conditions, theoretical distributions are created
ListConditions <- levels(DataTable$Conditions)
Area <- DataTable$Area[1]
ModelDataTable <- CreateModelDataTable(Model, ListConditions, Area, Law="BlackLaw", Scale="Lognormal")
# Display a few information regarding the model: parameters, goodness of fit...
FitResultsDisplay(Model, DataTable, DeviceID)
return(ModelDataTable)
}
#' Electromigration data analysis
#'
#' Extract Black's parameters from a set of electromigration experiments.
#' The experimental data as well as the resulting model are displayed and
#' can be saved. Extracted parameters are saved in a fit.txt file.
#'
#' @param ErrorBand displays the confidence intervals if set to TRUE.
#' @param ConfidenceValue percentage used in the confidence interval calculation
#' @param Save saves the chart as .png if set to TRUE.
#'
#' @return None
#'
#' @examples
#' BlackAnalysis()
#' BlackAnalysis(ErrorBand=FALSE)
#' @author Emmanuel Chery, \email{emmanuel.chery@@ams.com}
#' @import ggplot2 MASS scales nlstools tcltk
#' @export
BlackAnalysis <- function(ErrorBand=FALSE, ConfidenceValue=0.95, Save=TRUE)
{
# Disable warning for this function.
oldw <- getOption("warn")
options(warn = -1)
#rm(list=ls())
# ListFiles <- list.files(pattern="*exportfile.txt")
# Filters for file selection
Filters <- matrix(c("All files", "*", "Text", ".txt", "Export Files", "*exportfile.txt"),3, 2, byrow = TRUE)
ListFiles <- SelectFilesAdvanced(Filters)
# case 1, there are one or several files available
if (length(ListFiles) != 0){
# List of DeviceID available in the selected exportfiles
DeviceIDList <- levels(sapply(ListFiles,function(x){factor(strsplit(x,split="_")[[1]][2])}))
for (DeviceID in DeviceIDList){
SubListFiles <- ListFiles[grep(DeviceID,ListFiles)]
# Import the file(s) and create the 3 dataframes + display data
DataTable <- try(ReadDataAce(SubListFiles), silent=TRUE)
if (class(DataTable) != "try-error"){
# Reading the file was ok.
# Try to import the area
if (class(AddArea(DataTable, DeviceID)) != "try-error" ){
DataTable <- AddArea(DataTable, DeviceID)
}
# Modelization, errorBands calculation and Graph is made with a clean table where only failed samples are kept.
# DataTable is kept in order to be saved in fit.txt
CleanExpDataTable <- KeepOnlyFailed(DataTable)
# Attempt to modelize. If succes, we plot the chart, otherwise we only plot the data.
ModelDataTable <- try(BlackModelization(CleanExpDataTable, DeviceID),silent=TRUE)
if (class(ModelDataTable) != "try-error"){
if (ErrorBand){
ErrorDataTable <- ErrorEstimation(CleanExpDataTable, ModelDataTable, ConfidenceValue)
} else {
ErrorDataTable <- NULL
}
CreateGraph(CleanExpDataTable, ModelDataTable, ErrorDataTable, aesVec = c("TTF", "Probability", "Conditions"), title = DeviceID,
axisTitles = c("Time to Failure (s)","Probability (%)"), scale.x = "Log", scale.y = "Lognormal", save = Save )
# ExpData are added to the fit.txt file created during modelization
SaveData2File(DataTable, "fit.txt")
# There was an error either with Area or with modelization, we go to fallback mode
} else { # if modelization is not a success, we display the data and return parameters of the distribution in the console (scale and loc) in case user need them.
ModelDataTable <- FitDistribution(CleanExpDataTable,Scale="Lognormal")
CreateGraph(CleanExpDataTable, ModelDataTable, aesVec = c("TTF", "Probability", "Conditions"), title = DeviceID,
axisTitles = c("Time to Failure (s)","Probability (%)"), scale.x = "Log", scale.y = "Lognormal", save = FALSE)
}
} else { # reading the files returned an error.
print("Error detected in the file(s) you selected. Please check your selection.")
}
}
} else { # case 2, there are no files available
print("You need to create the export files first!")
}
# return(DataTable)
# Warning are set on again.
options(warn = oldw)
}
BlackModelization.me <- function(DataTable, DeviceID)
# Modelize the data using Black equation
# Extract the parameters: A, n and Ea
# as well as the lognormal slope
# TTF = A j^(-n) exp(Ea/kT + Scale * Proba)
# Proba in standard deviations
# Data(TTF,Status,Probability,Conditions,Stress,Temperature, Dimension)
{
# Read the list of device to retrieve the section parameters.
ListDevice <- try(read.delim("//fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt"),silent=TRUE)
#if file is not present, error is returned.
if (class(ListDevice) == "try-error"){
print("File //fsup04/fntquap/Common/Qual/Process_Reliability/Process/amsReliability_R_Package/ListDeviceName.txt not found.")
return(ListDevice)
} else {
W <- ListDevice$Width[ListDevice$Device==DeviceID] # micrometers
H <- ListDevice$Height[ListDevice$Device==DeviceID] # micrometers
S <- W*H*1E-12 # m^2
# if S is a positive number different from 0, we can proceed:
if (is.na(S) || S<=0 ) {
print(paste("Structure",DeviceID, "is not present in the list. Please fill the list!"))
# Force an error in the return for BlackAnalysis.
ModelDataTable <- data.frame()
as(ModelDataTable,"try-error")
return(ModelDataTable)
} else { # we proceed
# Physical constants
k <- 1.38E-23 # Boltzmann
e <- 1.6E-19 # electron charge
# Remove the units where status is 0
CleanDataTable <- DataTable[DataTable$Status==1,]
B.f <- deriv(~log(exp(A)*(Stress*1E-3/1)^(-n)*exp((Ea*1.6E-19)/(1.38E-23*(Temperature+273.15))+Scale*Probability))/log(10),namevec = c('A','Ea','n','Scale'), function.arg = c('A','Ea','n','Scale','Stress', 'Temperature','Probability'))
y = log10(DataTable$TTF)
fit.nlmer <- nlmer(log10(y) ~ B.f(A,Ea,n,Scale,Stress, Temperature,Probability) ~ Temperature | Conditions, start=list(nlpars=c(A=30,Ea=0.7,n=2,Scale=0.3)), data=DataTable)
summary(fit.nlmer)
# Parameters Extraction
A <- coef(Model)[1]
n <- coef(Model)[2]
Ea <-coef(Model)[3]
Scale <- coef(Model)[4]
# Residual Sum of Squares
RSS <- sum(resid(Model)^2)
# Total Sum of Squares: TSS <- sum((TTF - mean(TTF))^2))
TSS <- sum(sapply(split(CleanDataTable[,1],CleanDataTable$Conditions),function(x) sum((x-mean(x))^2)))
Rsq <- 1-RSS/TSS # R-squared measure
#print(paste("Size on 150 rows:", format(object.size(Model), unit="Mb")))
# Using the parameters and the conditions, theoretical distributions are created
ListConditions <- levels(CleanDataTable$Conditions)
# Initialisation
ModelDataTable <- data.frame()
# y axis points are calculated. (limits 0.01% -- 99.99%) Necessary to have nice confidence bands.
Proba <- seq(qnorm(0.0001),qnorm(0.9999),0.05)
for (i in seq_along(ListConditions)){
# Experimental conditions:
Condition <- ListConditions[i]
I <- CleanDataTable$Stress[CleanDataTable$Conditions==Condition][1]
Temp <- CleanDataTable$Temperature[CleanDataTable$Conditions==Condition][1] # °C
# TTF calculation
TTF <- exp(A)*(I*0.001/S)^(-n)*exp((Ea*e)/(k*(273.15+Temp))+ Proba * Scale)
# Dataframe creation
ModelDataTable <- rbind(ModelDataTable, data.frame('TTF'=TTF,'Status'=1,'Probability'=Proba,'Conditions'=Condition,'Stress'=I,'Temperature'=Temp))
}
# Drawing of the residual plots
plot(nlsResiduals(Model))
# Display of fit results
print(DeviceID)
print(summary(Model))
print(paste("Residual squared sum: ",RSS,sep=""))
#print(coef(Model))
#print(sd(resid(Model)))
# Save in a file
capture.output(summary(Model),file="fit.txt")
cat("Residual Squared sum:\t",file="fit.txt",append=TRUE)
cat(RSS,file="fit.txt",append=TRUE)
cat("\n \n",file="fit.txt",append=TRUE)
cat("Experimental Data:",file="fit.txt",append=TRUE)
cat("\n",file="fit.txt",append=TRUE)
capture.output(DataTable,file="fit.txt",append=TRUE)
return(ModelDataTable)
}
}
}
AdjustCensor <- function(DataTable)
# Adjust the censoring level
# Handle case where some unfailed samples have a lower TTF than finished ones.
# Return the DataTable with adjusted status
# DataTable(TTF,Status,Probability,Conditions,Stress,Temperature, Dimension)
{
# List of available conditions
listConditions <- levels(as.factor(DataTable$Conditions))
for (cond in listConditions){
minTimeOngoingSample <- min(DataTable$TTF[DataTable$Status==0 & DataTable$Conditions == cond])
DataTable$Status[DataTable$Status==1 & DataTable$TTF > minTimeOngoingSample & DataTable$Conditions == cond] <- 0
}
return(DataTable)
}
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