R/map_wafer.R
In DRManager/waferscopeR: Grammar for wafer level uniformity analysis
Defines functions
map_wafer
Documented in
map_wafer
#' Plot wafer contour maps
#'
#' Given thickness profile of a wafer, this function plots wafer maps including radial and residual decomposition
#' @param wafer dataframe from the wafer list (output of read.kla_f5, or a self-imported file obeys the same data structure)
#' @param resolution takes value of '\code{high}', '\code{normal}', '\code{low}', '\code{rapid}'
#' @param point '\code{on} (\code{off})': display (hide) point label,
#' @param maps '\code{mean}' shows the mean map only, '\code{decompose}' all the radial and residual maps, '\code{all}' includes all above three maps and radial spatial distribution
#' @param wlabel label of the output plot
#' @return coresponding wafer map(s)
#' @export
#' @import grDevices
#plot wafer maps with the input of wafer data
map_wafer <- function(wafer, resolution="normal", point="off",maps="mean", wlabel ="Wafer Map"){
#pass wafer data as data frame with x,y,thickness and/or id ->thickness, GOF, X,Y
#calcuate mean, radial, residual
#output four charts: mean map, radial distribution, radial and residual maps
#colnames(wafer) <- c("X","Y","ThK")
# resolution 2: normal, 1: high, 3: low, 5: ultra low
step <- switch(resolution,
"high" = 1,
"normal" =2,
"low" = 3,
"rapid" =5)
#create a data.frame with x,y,z columns
wafer.raw <- data.frame(x = round(wafer[,"X"], 1), y = round(wafer[,"Y"], 1), z = round(wafer[,"ThK"], 1))
if (maps == "mean") {
#wafer statistics
wafer.mean <- round(mean(wafer.raw$z, na.rm = T),1)
wafer.3sigma <- round(3*sd(wafer.raw$z, na.rm = T),1)
percent_3sigma <- paste(round(100*wafer.3sigma/wafer.mean, 1), "%", sep="")
wafer.range <- round(max(wafer.raw$z, na.rm = T)-min(wafer.raw$z, na.rm = T),1)
percent_range <- paste(round(100*wafer.range/wafer.mean, 1), "%", sep="")
wafer.stat <- paste("Mean: ", wafer.mean, 'A \n', '3s: ', wafer.3sigma, 'A (',
percent_3sigma,') \n', 'Range: ', wafer.range, 'A (', percent_range, ')', sep = '')
#model to smoothen data
wafer.loess <- loess(z ~ x*y, data = wafer.raw, degree = 2, span = 0.25)
#expand grid, -150:150 -> 300 mm square
wafer.square <- expand.grid(list(x = seq(-150, 150, step), y = seq(-150, 150, step)))
#predict z values based on the model
z <- predict(wafer.loess, newdata = wafer.square)
wafer.square$z <- round(as.numeric(z),2)
#make a subset for 300 mm wafer
wafer.round <- subset(wafer.square, x^2+y^2 <= 150^2)
#color for surface plot
jet.colors <- # function from grDevices package
colorRampPalette(c('#FF00FF',"#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red"))
colorzjet <- jet.colors(20) # 100 separate color
#for text on map
name.hori <- wafer.raw[wafer.raw$y != 0,]
name.vert <- wafer.raw[wafer.raw$y == 0,]
#mean map
if (point =="off") {
#mean map
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) + ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size=rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1)
} else{
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) +
ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size=rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+
scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
geom_point(data = wafer.raw, aes(x = wafer.raw$x, y = wafer.raw$y, label=as.character(wafer.raw$z)), size = 2)+
geom_text(aes(x = name.hori$x, y = name.hori$y, label=as.character(name.hori$z)),
size = 3,hjust=0, vjust=0, nudge_x = 0.5, angle = 0)+
geom_text(aes(x = name.vert$x, y = name.vert$y, label=as.character(name.vert$z)),
size = 3,hjust=0, vjust=0,nudge_y = 0.5, angle = 60)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
}
return(plot.mean) # mean wafer map only
}
else {
#wafer statistics
wafer.mean <- round(mean(wafer.raw$z, na.rm = T),1)
wafer.3sigma <- round(3*sd(wafer.raw$z, na.rm = T),1)
percent_3sigma <- paste(round(100*wafer.3sigma/wafer.mean, 1), "%", sep="")
wafer.range <- round(max(wafer.raw$z, na.rm = T)-min(wafer.raw$z, na.rm = T),1)
percent_range <- paste(round(100*wafer.range/wafer.mean, 1), "%", sep="")
wafer.stat <- paste("Mean: ", wafer.mean, 'A \n', '3s: ', wafer.3sigma, 'A (',
percent_3sigma,') \n', 'Range: ', wafer.range, 'A (', percent_range, ')', sep = '')
#radial component
wafer.raw$r <- round(sqrt(wafer.raw[,1]^2+wafer.raw[,2]^2),1)
r.unique <- unique(wafer.raw$r)
wafer.radial <- data.frame()
for (i in 1:length(r.unique)){
wafer.samer <- subset(wafer.raw, wafer.raw$r == r.unique[i])
wafer.samer$zmean <- round(mean(wafer.samer$z),1)
wafer.radial <- rbind(wafer.radial, wafer.samer)
}
#wafer stat - radial
radial.3sigma <- round(3*sd(wafer.radial$zmean, na.rm = T),1)
radial.percent_3sigma <- paste(round(100*radial.3sigma/wafer.mean, 1), "%", sep="")
radial.range <- round(max(wafer.radial$zmean, na.rm = T)-min(wafer.radial$zmean, na.rm = T),1)
radial.percent_range <- paste(round(100*radial.range/wafer.mean, 1), "%", sep="")
radial.stat <- paste('3s: ', radial.3sigma, 'A (',
radial.percent_3sigma,') \n', 'Range: ', radial.range, 'A (', radial.percent_range, ')', sep = '')
#wafer stat - residual
resi <- wafer.radial
resi$z <- wafer.radial$z - wafer.radial$zmean
resi.3sigma <- round(3*sd(resi$z, na.rm = T),1)
resi.range <- round(max(resi$z, na.rm = T)-min(resi$z, na.rm = T),1)
resi.stat <- paste('3s: ', resi.3sigma, 'A ',
' \n', 'Range: ', resi.range, 'A ', sep = '')
#model to smoothen data
wafer.loess <- loess(z ~ x*y, data = wafer.raw, degree = 2, span = 0.25)
wafer.radial.loess <- loess(zmean ~ x*y, data = wafer.radial, degree = 2, span = 0.25)
#expand grid, -150:150 -> 300 mm square
wafer.square <- expand.grid(list(x = seq(-150, 150, step), y = seq(-150, 150, step)))
wafer.radial.square <- expand.grid(list(x = seq(-150, 150, step), y = seq(-150, 150, step)))
#predict z values based on the model
z <- predict(wafer.loess, newdata = wafer.square)
wafer.square$z <- round(as.numeric(z),2)
zmean <- predict(wafer.radial.loess, newdata = wafer.radial.square)
wafer.radial.square$zmean <- round(as.numeric(zmean),2)
wafer.radial.square$r <- round(sqrt(wafer.radial.square$x^2+wafer.radial.square$y^2),0)
#make a subset for 300 mm wafer
wafer.round <- subset(wafer.square, x^2+y^2 <= 150^2)
wafer.radial.round <- subset(wafer.radial.square, x^2+y^2 <= 150^2)
radial.test <- wafer.radial.round
r.unique <- unique(radial.test$r)
test <- data.frame()
wafer.radial.avg <- data.frame()
for (i in 1:length(r.unique)){
wafer.samer <- subset(radial.test, radial.test$r == r.unique[i])
xy.unique <- unique(abs(wafer.samer$x))
x.unique <- xy.unique[xy.unique <= round(r.unique[i]/sqrt(2),0)]
for (j in 1:length(x.unique)){
test <- wafer.samer[abs(wafer.samer$x) == x.unique[j] | abs(wafer.samer$y) == x.unique[j],]
zmeanmean <- mean(test$zmean, na.rm = T)
wafer.samer[abs(wafer.samer$x) == x.unique[j] | abs(wafer.samer$y) == x.unique[j],]$zmean <- zmeanmean
}
radial.test[radial.test$r == r.unique[i],]$zmean <- wafer.samer$zmean
}
wafer.radial.round <- radial.test
#residual
residual <- data.frame(x = wafer.round$x, y = wafer.round$y, z= wafer.round$z - wafer.radial.round$zmean)
#color for surface plot
jet.colors <- # function from grDevices package
colorRampPalette(c('#FF00FF',"#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red"))
colorzjet <- jet.colors(20) # 100 separate color
#for text on map
name.hori <- wafer.raw[wafer.raw$y != 0,]
name.vert <- wafer.raw[wafer.raw$y == 0,]
#mean map
if (point =="off") {
#mean map
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) + ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1)
} else{
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) +
ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+
scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
geom_point(data = wafer.raw, aes(x = wafer.raw$x, y = wafer.raw$y, label=as.character(wafer.raw$z),size=0.5), size = 2)+
geom_text(aes(x = name.hori$x, y = name.hori$y, label=as.character(name.hori$z)),
size = 2,hjust=0, vjust=0, nudge_x = 1, angle = 0)+
geom_text(aes(x = name.vert$x, y = name.vert$y, label=as.character(name.vert$z)),
size = 3,hjust=0, vjust=0,nudge_y = 0.5, angle = 0)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
}
# radial
plot.radial <- ggplot(wafer.radial.round, aes(wafer.radial.round$x,wafer.radial.round$y,z=wafer.radial.round$zmean))+
geom_tile(aes(fill=zmean))+
xlab(radial.stat) + ylab('Y(mm)')+ggtitle('Radial Average')+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
# residual
plot.residual <- ggplot(residual, aes(residual$x, residual$y, z=residual$z))+
geom_tile(aes(fill=z))+
xlab(resi.stat) + ylab('Y(mm)')+ggtitle('Residuals')+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
# Radial distribution
plot.distribution <- ggplot(wafer.raw, aes(r,z)) + geom_point(size=2, color="blue") +
xlab("Wafer Radius (mm)") + ylab("Etch Amount (A)") + xlim(0,150) + ggtitle ("Radial Distribution")
# Arrange the output with gridExtra package
gridExtra::grid.arrange(plot.mean, plot.radial,plot.distribution,plot.residual)
#return(plist)
}
}
DRManager/waferscopeR documentation built on May 6, 2019, 1:18 p.m.
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Defines functions map_wafer
Documented in map_wafer
#' Plot wafer contour maps
#'
#' Given thickness profile of a wafer, this function plots wafer maps including radial and residual decomposition
#' @param wafer dataframe from the wafer list (output of read.kla_f5, or a self-imported file obeys the same data structure)
#' @param resolution takes value of '\code{high}', '\code{normal}', '\code{low}', '\code{rapid}'
#' @param point '\code{on} (\code{off})': display (hide) point label,
#' @param maps '\code{mean}' shows the mean map only, '\code{decompose}' all the radial and residual maps, '\code{all}' includes all above three maps and radial spatial distribution
#' @param wlabel label of the output plot
#' @return coresponding wafer map(s)
#' @export
#' @import grDevices
#plot wafer maps with the input of wafer data
map_wafer <- function(wafer, resolution="normal", point="off",maps="mean", wlabel ="Wafer Map"){
#pass wafer data as data frame with x,y,thickness and/or id ->thickness, GOF, X,Y
#calcuate mean, radial, residual
#output four charts: mean map, radial distribution, radial and residual maps
#colnames(wafer) <- c("X","Y","ThK")
# resolution 2: normal, 1: high, 3: low, 5: ultra low
step <- switch(resolution,
"high" = 1,
"normal" =2,
"low" = 3,
"rapid" =5)
#create a data.frame with x,y,z columns
wafer.raw <- data.frame(x = round(wafer[,"X"], 1), y = round(wafer[,"Y"], 1), z = round(wafer[,"ThK"], 1))
if (maps == "mean") {
#wafer statistics
wafer.mean <- round(mean(wafer.raw$z, na.rm = T),1)
wafer.3sigma <- round(3*sd(wafer.raw$z, na.rm = T),1)
percent_3sigma <- paste(round(100*wafer.3sigma/wafer.mean, 1), "%", sep="")
wafer.range <- round(max(wafer.raw$z, na.rm = T)-min(wafer.raw$z, na.rm = T),1)
percent_range <- paste(round(100*wafer.range/wafer.mean, 1), "%", sep="")
wafer.stat <- paste("Mean: ", wafer.mean, 'A \n', '3s: ', wafer.3sigma, 'A (',
percent_3sigma,') \n', 'Range: ', wafer.range, 'A (', percent_range, ')', sep = '')
#model to smoothen data
wafer.loess <- loess(z ~ x*y, data = wafer.raw, degree = 2, span = 0.25)
#expand grid, -150:150 -> 300 mm square
wafer.square <- expand.grid(list(x = seq(-150, 150, step), y = seq(-150, 150, step)))
#predict z values based on the model
z <- predict(wafer.loess, newdata = wafer.square)
wafer.square$z <- round(as.numeric(z),2)
#make a subset for 300 mm wafer
wafer.round <- subset(wafer.square, x^2+y^2 <= 150^2)
#color for surface plot
jet.colors <- # function from grDevices package
colorRampPalette(c('#FF00FF',"#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red"))
colorzjet <- jet.colors(20) # 100 separate color
#for text on map
name.hori <- wafer.raw[wafer.raw$y != 0,]
name.vert <- wafer.raw[wafer.raw$y == 0,]
#mean map
if (point =="off") {
#mean map
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) + ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size=rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1)
} else{
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) +
ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size=rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+
scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
geom_point(data = wafer.raw, aes(x = wafer.raw$x, y = wafer.raw$y, label=as.character(wafer.raw$z)), size = 2)+
geom_text(aes(x = name.hori$x, y = name.hori$y, label=as.character(name.hori$z)),
size = 3,hjust=0, vjust=0, nudge_x = 0.5, angle = 0)+
geom_text(aes(x = name.vert$x, y = name.vert$y, label=as.character(name.vert$z)),
size = 3,hjust=0, vjust=0,nudge_y = 0.5, angle = 60)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
}
return(plot.mean) # mean wafer map only
}
else {
#wafer statistics
wafer.mean <- round(mean(wafer.raw$z, na.rm = T),1)
wafer.3sigma <- round(3*sd(wafer.raw$z, na.rm = T),1)
percent_3sigma <- paste(round(100*wafer.3sigma/wafer.mean, 1), "%", sep="")
wafer.range <- round(max(wafer.raw$z, na.rm = T)-min(wafer.raw$z, na.rm = T),1)
percent_range <- paste(round(100*wafer.range/wafer.mean, 1), "%", sep="")
wafer.stat <- paste("Mean: ", wafer.mean, 'A \n', '3s: ', wafer.3sigma, 'A (',
percent_3sigma,') \n', 'Range: ', wafer.range, 'A (', percent_range, ')', sep = '')
#radial component
wafer.raw$r <- round(sqrt(wafer.raw[,1]^2+wafer.raw[,2]^2),1)
r.unique <- unique(wafer.raw$r)
wafer.radial <- data.frame()
for (i in 1:length(r.unique)){
wafer.samer <- subset(wafer.raw, wafer.raw$r == r.unique[i])
wafer.samer$zmean <- round(mean(wafer.samer$z),1)
wafer.radial <- rbind(wafer.radial, wafer.samer)
}
#wafer stat - radial
radial.3sigma <- round(3*sd(wafer.radial$zmean, na.rm = T),1)
radial.percent_3sigma <- paste(round(100*radial.3sigma/wafer.mean, 1), "%", sep="")
radial.range <- round(max(wafer.radial$zmean, na.rm = T)-min(wafer.radial$zmean, na.rm = T),1)
radial.percent_range <- paste(round(100*radial.range/wafer.mean, 1), "%", sep="")
radial.stat <- paste('3s: ', radial.3sigma, 'A (',
radial.percent_3sigma,') \n', 'Range: ', radial.range, 'A (', radial.percent_range, ')', sep = '')
#wafer stat - residual
resi <- wafer.radial
resi$z <- wafer.radial$z - wafer.radial$zmean
resi.3sigma <- round(3*sd(resi$z, na.rm = T),1)
resi.range <- round(max(resi$z, na.rm = T)-min(resi$z, na.rm = T),1)
resi.stat <- paste('3s: ', resi.3sigma, 'A ',
' \n', 'Range: ', resi.range, 'A ', sep = '')
#model to smoothen data
wafer.loess <- loess(z ~ x*y, data = wafer.raw, degree = 2, span = 0.25)
wafer.radial.loess <- loess(zmean ~ x*y, data = wafer.radial, degree = 2, span = 0.25)
#expand grid, -150:150 -> 300 mm square
wafer.square <- expand.grid(list(x = seq(-150, 150, step), y = seq(-150, 150, step)))
wafer.radial.square <- expand.grid(list(x = seq(-150, 150, step), y = seq(-150, 150, step)))
#predict z values based on the model
z <- predict(wafer.loess, newdata = wafer.square)
wafer.square$z <- round(as.numeric(z),2)
zmean <- predict(wafer.radial.loess, newdata = wafer.radial.square)
wafer.radial.square$zmean <- round(as.numeric(zmean),2)
wafer.radial.square$r <- round(sqrt(wafer.radial.square$x^2+wafer.radial.square$y^2),0)
#make a subset for 300 mm wafer
wafer.round <- subset(wafer.square, x^2+y^2 <= 150^2)
wafer.radial.round <- subset(wafer.radial.square, x^2+y^2 <= 150^2)
radial.test <- wafer.radial.round
r.unique <- unique(radial.test$r)
test <- data.frame()
wafer.radial.avg <- data.frame()
for (i in 1:length(r.unique)){
wafer.samer <- subset(radial.test, radial.test$r == r.unique[i])
xy.unique <- unique(abs(wafer.samer$x))
x.unique <- xy.unique[xy.unique <= round(r.unique[i]/sqrt(2),0)]
for (j in 1:length(x.unique)){
test <- wafer.samer[abs(wafer.samer$x) == x.unique[j] | abs(wafer.samer$y) == x.unique[j],]
zmeanmean <- mean(test$zmean, na.rm = T)
wafer.samer[abs(wafer.samer$x) == x.unique[j] | abs(wafer.samer$y) == x.unique[j],]$zmean <- zmeanmean
}
radial.test[radial.test$r == r.unique[i],]$zmean <- wafer.samer$zmean
}
wafer.radial.round <- radial.test
#residual
residual <- data.frame(x = wafer.round$x, y = wafer.round$y, z= wafer.round$z - wafer.radial.round$zmean)
#color for surface plot
jet.colors <- # function from grDevices package
colorRampPalette(c('#FF00FF',"#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red"))
colorzjet <- jet.colors(20) # 100 separate color
#for text on map
name.hori <- wafer.raw[wafer.raw$y != 0,]
name.vert <- wafer.raw[wafer.raw$y == 0,]
#mean map
if (point =="off") {
#mean map
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) + ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1)
} else{
plot.mean <- ggplot()+
geom_tile(data = wafer.round, aes(x = wafer.round$x,y = wafer.round$y,fill=wafer.round$z))+
xlab(wafer.stat) +
ylab('Y(mm)')+ggtitle(wlabel)+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+
scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
geom_point(data = wafer.raw, aes(x = wafer.raw$x, y = wafer.raw$y, label=as.character(wafer.raw$z),size=0.5), size = 2)+
geom_text(aes(x = name.hori$x, y = name.hori$y, label=as.character(name.hori$z)),
size = 2,hjust=0, vjust=0, nudge_x = 1, angle = 0)+
geom_text(aes(x = name.vert$x, y = name.vert$y, label=as.character(name.vert$z)),
size = 3,hjust=0, vjust=0,nudge_y = 0.5, angle = 0)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
}
# radial
plot.radial <- ggplot(wafer.radial.round, aes(wafer.radial.round$x,wafer.radial.round$y,z=wafer.radial.round$zmean))+
geom_tile(aes(fill=zmean))+
xlab(radial.stat) + ylab('Y(mm)')+ggtitle('Radial Average')+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
# residual
plot.residual <- ggplot(residual, aes(residual$x, residual$y, z=residual$z))+
geom_tile(aes(fill=z))+
xlab(resi.stat) + ylab('Y(mm)')+ggtitle('Residuals')+
theme(axis.line=element_blank(),axis.text.y=element_blank(), axis.text.x=element_blank(),
axis.ticks=element_blank(),
axis.title.y=element_blank(),
axis.title=element_text(size=rel(1)),plot.title = element_text(size = rel(1)),
panel.background = element_rect(fill = "transparent"), panel.grid.minor=element_blank()
)+ scale_fill_gradientn(name = 'Etch Amount (A)', colours=colorzjet) + coord_fixed(ratio=1)+
annotate("path", x=150*cos(seq(0,2*pi,length.out=1000)), y=150*sin(seq(0,2*pi,length.out=1000)), size = 1.5)
# Radial distribution
plot.distribution <- ggplot(wafer.raw, aes(r,z)) + geom_point(size=2, color="blue") +
xlab("Wafer Radius (mm)") + ylab("Etch Amount (A)") + xlim(0,150) + ggtitle ("Radial Distribution")
# Arrange the output with gridExtra package
gridExtra::grid.arrange(plot.mean, plot.radial,plot.distribution,plot.residual)
#return(plist)
}
}
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