mica.select(concat_ws(':',mica.DieX, mica.DieY)).show()

or use UDF:

func = udf(lambda c1,c2: c1 +":" + c2, StringType())
mica.withColumn("fid", func(col('LotId'),col('WaferId'))).show()

Define an UDF
def position_X(s):
    if s in ["0","1","2","3","4","5","6","7","8","9"]:
        return 'P0'+s
    elif s in ["-1","-2","-3","-4","-5","-6","-7","-8","-9"]:
        return 'N0'+s[1]
    elif s >= "10":
        return 'P' + s
    else:
        return 'N' + s[1:]
spark.udf.register("position_X",position_X)

def position_Y(s):
    if len(s) == 1:
        return '0' + s
    else:
        return s
spark.udf.register("position_Y",position_Y)

from pyspark.sql.functions import *
from pyspark.sql.functions import udf,col
from pyspark.sql.types import *

pdf = pdf.select('*',substring("LotId",1,7) ,substring("WaferId",6,2)).drop("LotId","WaferId")
pdf = pdf.withColumnRenamed('substring(LotId, 1, 7)', 'LotId')
pdf = pdf.withColumnRenamed('substring(WaferId, 6, 2)', 'WaferId')
Die_X = udf(position_X, StringType())
pdf = pdf.withColumn("DieX", Die_X(col('DieX')))
Die_Y = udf(position_Y, StringType())
pdf = pdf.withColumn("DieY", Die_Y(col('DieY')))
pdf = pdf.withColumn('Fid',concat_ws(':',pdf.LotId, pdf.WaferId,pdf.DieX,pdf.DieY)).drop("DieX","DieY","LotId","WaferId")