CoCalc Public FilesSPC_3.ipynb
Author: phonchi chung
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Description: Jupyter notebook SPC_3.ipynb
In [1]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


## System Prototype

The basic Runto Run Controller

The W2W control scheme:

• The $y_k$ is either form AVM or Metrology tool
• $A_k$ (or A) is typically chosen to be least square estimates of $β_1$ based on historical data.The control valuable is set to nullify the deviation from target.

### CMP Example

1. $y_k$ is the actual removal amount measured from the metrology tool and $PostY_k$ is the actual post CMP thickness of run k. The specification of $PostY_k$ is 2800±150 Angstrom (Å) with 2800 being the target value denoted by $TgtPostY$ Deifned $PreY_k$ be the one before CMP process, $ARR_k$ is the polish rate, $\mu_k$ is the polish time that we can control!

The material removal model for CMP can be divided into two parts, mechanical model and chemical model. The chemical action of slurry is responsible for continuosly softening the silicon oxide. The fresh silicon oxide or metal surface is then rapidly removed by mechanical part.

1. The $A_k$ is the nominal removal rate, which is empirically simulated by a polynomial curve fitting of parts usage count between PMs (denoted by PU varying from 1 to 600)
1. Process gain:
1. Simulation parameters:
1. Assumption:
1. $A_k$ is set to 300, and A is set to mean of $ARR_k$ (also 300)
2. $\beta_0$ is set to 700 and $b_0$ is 500
3. RI is simulated with 0.9 plus some variation, and assume controller can detect any value that is beyond UCL or LCL
4. yk from AVM is virtual times yz(actual metrodlogy data), where virtual is a normal distribution variable
5. $\eta_k$ is simulated as ARIMA(0 1 1)
6. Control $\beta$ not $\eta$

yz is atual measurement value, and A is used internal by R2R

In [2]:
from statsmodels.tsa.arima_model import ARIMA
import sklearn.linear_model as skl_lm

In [23]:
# Simulated environment
Error   = np.random.normal(0, 25,    600)
PM1     = np.random.normal(0, 50,    600)
PM2     = np.random.normal(0, 3,      600)
Stress1 = np.random.normal(1000, 55,600)
Stress2 = np.random.normal(0, 4,     600)
Rotspd1 = np.random.normal(100, 5.5,   600)
Rotspd2 = np.random.normal(0, 0.4,    600)
Sfuspd1 = np.random.normal(100, 5.5,   600)
Sfuspd2 = np.random.normal(0, 0.4,    600)
PreY    = np.random.normal(3800, 70, 600)

TgtPostY = np.array([2800]*600)
# Assumption
Virtual = np.random.normal(1, np.sqrt(0.01),   600)
Ak0 = 1200 # Not sure, does not know PU
Ak = np.array([1200]*600)
RI = 0.9
etak0 = 0
etak = 500
TgtY = PreY - TgtPostY

def ARR (stress1, stress2, rotspd1, rotspd2, sfuspd1, sfuspd2, pm1, pm2, error):
return Ak0*((stress1+stress2)/1000)*((rotspd1+rotspd2)/100)*((sfuspd1+sfuspd2)/100)+pm1+pm2+error
ARRk  = ARR(Stress1, Stress2, Rotspd1, Rotspd2, Sfuspd1, Sfuspd2, PM1, PM2, Error) # beta1 actual process gain

RI=RI*np.sqrt(np.sqrt(Virtual))
# ARIMA (0 1 1) assume error
model = ARIMA(Error, order=(0, 1, 1))
results_AR = model.fit(disp=-1)

alpha1 = 0.35
etak = np.zeros(len(PreY))
mu = np.zeros(len(PreY))
PostY = np.zeros(len(PreY))
yz = np.zeros(len(PreY))

mu[0] = (TgtY[0] - etak0)/Ak[0]
yz[0] = ARRk[0]*mu[0]+ results_AR.fittedvalues[0]  # Actual value
PostY[0] = PreY[0] - yz[0]
etak[1] = alpha1*(yz[0]-Ak[0]*mu[0])+(1-alpha1)*etak0
mu[1] = (TgtY[1] - etak[1])/Ak[1]

yz[1] = ARRk[1]*mu[1]+ results_AR.fittedvalues[1]   # Actual value
PostY[1] = PreY[1] - yz[1]
etak[2] = alpha1*(yz[1]-Ak[1]*mu[1])+(1-alpha1)*etak[1]
mu[2] = (TgtY[2] - etak[2])/Ak[2]

for k in range(2,599):
yz[k] = ARRk[k]*mu[k]+ 700 + results_AR.fittedvalues[k] ## ARRk[k], Toub[k] approximate beta_1, beta_0(not tou!!)
PostY[k] = PreY[k] - yz[k]
etak[k+1] = alpha1*(yz[k]-Ak[k]*mu[k])+(1-alpha1)*etak[k]
mu[k+1] = (TgtY[k+1] - etak[k+1])/Ak[k+1]

# Regression coefficients (Ordinary Least Squares)
mu = mu.reshape((600,1))
yz = yz.reshape((600,1))
regr = skl_lm.LinearRegression()
regr.fit(mu,yz)

print(regr.intercept_)
print(regr.coef_)
print(regr.score(mu,yz))

[ 755.61616784] [[ 962.33970185]] 0.531339007585
In [22]:
# Simulated environment
Error   = np.random.normal(0, 25,    600)
PM1     = np.random.normal(0, 50,    600)
PM2     = np.random.normal(0, 3,      600)
Stress1 = np.random.normal(1000, 55,600)
Stress2 = np.random.normal(0, 4,     600)
Rotspd1 = np.random.normal(100, 5.5,   600)
Rotspd2 = np.random.normal(0, 0.4,    600)
Sfuspd1 = np.random.normal(100, 5.5,   600)
Sfuspd2 = np.random.normal(0, 0.4,    600)
PreY    = np.random.normal(3800, 70, 600)

TgtPostY = np.array([2800]*600)
# Assumption
Virtual = np.random.normal(1, np.sqrt(0.01),   600)
Ak0 = 1200 # Not sure, does not know PU
Ak = np.array([1200]*600)
RI = 0.9
etak0 = 0
etak = 500
TgtY = PreY - TgtPostY

def ARR (stress1, stress2, rotspd1, rotspd2, sfuspd1, sfuspd2, pm1, pm2, error):
return Ak0*((stress1+stress2)/1000)*((rotspd1+rotspd2)/100)*((sfuspd1+sfuspd2)/100)+pm1+pm2+error
ARRk  = ARR(Stress1, Stress2, Rotspd1, Rotspd2, Sfuspd1, Sfuspd2, PM1, PM2, Error) # beta1 actual process gain

# ARIMA (0 1 1) assume error
model = ARIMA(Error, order=(0, 1, 1))
results_AR = model.fit(disp=-1)

In [4]:
RI=RI*np.sqrt(np.sqrt(Virtual))


### Round 1

First two rounds, we use actual metrodlogy value

In [34]:
alpha1 = 0.35
etak = np.zeros(len(PreY))
mu = np.zeros(len(PreY))
PostY = np.zeros(len(PreY))
yz = np.zeros(len(PreY))

In [35]:
mu[0] = (TgtY[0] - etak0)/Ak[0]
yz[0] = ARRk[0]*mu[0]+ results_AR.fittedvalues[0]  # Actual value
PostY[0] = PreY[0] - yz[0]
etak[1] = alpha1*(yz[0]-Ak[0]*mu[0])+(1-alpha1)*etak0
mu[1] = (TgtY[1] - etak[1])/Ak[1]
print etak[1]
print yz[0]
print mu[0]
print mu[1]
print PostY[0]

9.4087832029 1064.34565718 0.864552849547 0.872436677123 2773.11776228

### Round2

In [36]:
yz[1] = ARRk[1]*mu[1]+ results_AR.fittedvalues[1]   # Actual value
PostY[1] = PreY[1] - yz[1]
etak[2] = alpha1*(yz[1]-Ak[1]*mu[1])+(1-alpha1)*etak[1]
mu[2] = (TgtY[2] - etak[2])/Ak[2]
print etak[2]
print yz[1]
print mu[2]
print PostY[1]

45.0780102323 1158.24487298 0.790866989964 2698.08792277

### CASE1 R2R with in-situ metrology

$𝛼_1$ set to 0.35, and all actual metrology data are available

In [37]:
for k in range(2,599):
yz[k] = ARRk[k]*mu[k]+ results_AR.fittedvalues[k] ## ARRk[k], Toub[k] approximate beta_1, beta_0(not tou!!)
PostY[k] = PreY[k] - yz[k]
etak[k+1] = alpha1*(yz[k]-Ak[k]*mu[k])+(1-alpha1)*etak[k]
mu[k+1] = (TgtY[k+1] - etak[k+1])/Ak[k+1]

In [38]:
for k in range(0, 7):
print etak[k+1]

9.4087832029 45.0780102323 24.864613292 5.37253882486 8.19373790403 38.2825699152 65.735506947
In [39]:
for k in range(0, 7):
print mu[k+1]

0.872436677123 0.790866989964 0.881409276899 0.822494223586 0.760932003591 0.769812147481 0.858734964209
In [40]:
for k in range(0, 7):
print yz[k]

1064.34565718 1158.24487298 936.365835502 1026.86410424 1000.42617593 1007.28023367 1040.49410984
In [41]:
for k in range(0, 7):
print PostY[k]

2773.11776228 2698.08792277 2857.75256269 2855.69164133 2791.9394312 2714.03190854 2721.56303705
In [32]:
x = range(2, 590)
plt.plot(x,etak[2:590])

[<matplotlib.lines.Line2D at 0x7f2d3dbdc690>]
In [33]:
# Plotting
x = range(10, 590)
U = np.empty(580)
L = np.empty(580)
UCL = 2950
LCL = 2650
U.fill(2950)
L.fill(2650)
plt.plot(x,U)
plt.plot(x,L)
plt.plot(x,PostY[10:590])
plt.ylim([2600, 3000])
np.mean(PostY[10:590])

2800.5189283424916

CpK(Process Capability)

In [11]:
Cpk=np.zeros(len(PreY))
MAPEp =np.zeros(len(PreY))

In [12]:
for k in range(2,599):
Cpk[k] = min((UCL-np.mean(PostY[1:k]))/(3*np.std(PostY[1:k], dtype=np.float64)), (np.mean(PostY[1:k])-LCL)/(3*np.std(PostY[1:k], dtype=np.float64)))
plt.plot(x,Cpk[10:590])
plt.ylim([0.3, 2.0])

(0.3, 2.0)

Mean-absolute-percentage error (MAPEp)

In [13]:
for k in range(2,599):
MAPEp[k] = sum(np.absolute((PostY[1:k]-2800)/2800))/k*100
plt.plot(x,MAPEp[10:590])
plt.ylim([0.7, 2.5])

(0.7, 2.5)

### CASE2 R2R+VM without RI

$𝛼_2=𝛼_1=0.35$, apply to following wafers

In [14]:
# Reinitialized
alpha1 = 0.35
betak = np.zeros(len(PreY))
mu = np.zeros(len(PreY))
PostY = np.zeros(len(PreY))
PostYv = np.zeros(len(PreY))
y = np.zeros(len(PreY))
yz = np.zeros(len(PreY))

# Round1
mu[0] = (TgtY[0] - betak0)/Ak[0]
yz[0] = ARRk[0]*mu[0]+beta0+results_AR.fittedvalues[0]  # Actual value
PostY[0] = PreY[0] - yz[0]
betak[1] = alpha1*(yz[0]-Ak[0]*mu[0])+(1-alpha1)*betak0
mu[1] = (TgtY[1] - betak[1])/Ak[1]
# Round2
yz[1] = ARRk[1]*mu[1]+beta0+results_AR.fittedvalues[1]  # Actual value
PostY[1] = PreY[1] - yz[1]
betak[2] = alpha1*(yz[1]-Ak[1]*mu[1])+(1-alpha1)*betak[1]
mu[2] = (TgtY[2] - betak[2])/Ak[2]

In [15]:
for k in range(2,599):
yz[k] = ARRk[k]*mu[k]+results_AR.fittedvalues[k]+beta0 # Metrology data
if(k%25)==1:
y[k] = yz[k]
else:
y[k] = yz[k]*Virtual[k]  # Assume VM value ok ARR=ARR_bar
PostY[k] = PreY[k] - yz[k]
betak[k+1] = alpha1*(y[k]-Ak[k]*mu[k])+(1-alpha1)*betak[k]
mu[k+1] = (TgtY[k+1] - betak[k+1])/Ak[k+1]

In [16]:
x = range(2, 590)
plt.plot(x,betak[2:590])

[<matplotlib.lines.Line2D at 0x7f68a7814c50>]
In [17]:
x = range(10, 590)
U = np.empty(580)
L = np.empty(580)
U.fill(2950)
L.fill(2650)
plt.plot(x,U)
plt.plot(x,L)
plt.plot(x,PostY[10:590])
np.mean(PostY[10:590])
plt.ylim([2600, 3000])

(2600, 3000)
In [18]:
Cpk=np.zeros(len(PreY))
MAPEp =np.zeros(len(PreY))

In [19]:
for k in range(2,599):
Cpk[k] = min((UCL-np.mean(PostY[1:k]))/(3*np.std(PostY[1:k], dtype=np.float64)), (np.mean(PostY[1:k])-LCL)/(3*np.std(PostY[1:k], dtype=np.float64)))
plt.plot(x,Cpk[10:590])
plt.ylim([0.3, 2.0])

(0.3, 2.0)
In [20]:
for k in range(2,599):
MAPEp[k] = sum(np.absolute((PostY[1:k]-2800)/2800))/k*100
plt.plot(x,MAPEp[10:590])
plt.ylim([0.7, 2.5])

(0.7, 2.5)

### CASE3: R2R+VM with RI

In [21]:
# Reinitialized
alpha1 = 0.35
betak = np.zeros(len(PreY))
mu = np.zeros(len(PreY))
PostY = np.zeros(len(PreY))
PostYv = np.zeros(len(PreY))
y = np.zeros(len(PreY))
yz = np.zeros(len(PreY))

# Round1
mu[0] = (TgtY[0] - betak0)/Ak[0]
yz[0] = ARRk[0]*mu[0]+beta0+results_AR.fittedvalues[0]  # Actual value
PostY[0] = PreY[0] - yz[0]
betak[1] = alpha1*(yz[0]-Ak[0]*mu[0])+(1-alpha1)*betak0
mu[1] = (TgtY[1] - betak[1])/Ak[1]
# Round2
yz[1] = ARRk[1]*mu[1]+beta0+results_AR.fittedvalues[1]  # Actual value
PostY[1] = PreY[1] - yz[1]
betak[2] = alpha1*(yz[1]-Ak[1]*mu[1])+(1-alpha1)*betak[1]
mu[2] = (TgtY[2] - betak[2])/Ak[2]

In [22]:
for k in range(2,599):
yz[k] =  ARRk[k]*mu[k]+beta0+results_AR.fittedvalues[k] # process output how to filter out
if(k%25)==1:
y[k] =  yz[k]
alpha = alpha1
else:
y[k] =  yz[k]*Virtual[k]
alpha = RI[k]*alpha1
PostYv[k] = PreY[k] -  yz[k]*Virtual[k]
if PostYv[k]>2950 or PostYv[k]<2650:
alpha = 0
betak[k+1] = alpha*(y[k]-Ak[k]*mu[k])+(1-alpha)*betak[k]  #Toub canceled out
mu[k+1] = (TgtY[k+1] - betak[k+1])/Ak[k+1]
PostY[k] = PreY[k] -  yz[k] # Process output

In [23]:
x = range(2, 590)
plt.plot(x,betak[2:590])

[<matplotlib.lines.Line2D at 0x7f68a74fb850>]
In [24]:
x = range(10, 590)
U = np.empty(580)
L = np.empty(580)
U.fill(2950)
L.fill(2650)
# plot(x, PostY[3:600], type="o")
plt.plot(x,U)
plt.plot(x,L)
plt.plot(x,PostY[10:590])
np.mean(PostY[10:590])
plt.ylim([2600, 3000])

(2600, 3000)
In [25]:
Cpk=np.zeros(len(PreY))
MAPEp =np.zeros(len(PreY))

In [26]:
for k in range(2,599):
Cpk[k] = min((UCL-np.mean(PostY[1:k]))/(3*np.std(PostY[1:k], dtype=np.float64)), (np.mean(PostY[1:k])-LCL)/(3*np.std(PostY[1:k], dtype=np.float64)))
plt.plot(x,Cpk[10:590])
plt.ylim([0.3, 2.0])

(0.3, 2.0)
In [27]:
for k in range(2,599):
MAPEp[k] = sum(np.absolute((PostY[1:k]-2800)/2800))/k*100
plt.plot(x,MAPEp[10:590])
plt.ylim([0.7, 2.5])

(0.7, 2.5)

### CASE4: R2R+VM with (1-RI)

In [28]:
# Reinitialized
alpha1 = 0.35
betak = np.zeros(len(PreY))
mu = np.zeros(len(PreY))
PostY = np.zeros(len(PreY))
PostYv = np.zeros(len(PreY))
y = np.zeros(len(PreY))
yz = np.zeros(len(PreY))

# Round1
mu[0] = (TgtY[0] - betak0)/Ak[0]
yz[0] = ARRk[0]*mu[0]+beta0+results_AR.fittedvalues[0]  # Actual value
PostY[0] = PreY[0] - yz[0]
betak[1] = alpha1*(yz[0]-Ak[0]*mu[0])+(1-alpha1)*betak0
mu[1] = (TgtY[1] - betak[1])/Ak[1]
# Round2
yz[1] = ARRk[1]*mu[1]+beta0+results_AR.fittedvalues[1]  # Actual value
PostY[1] = PreY[1] - yz[1]
betak[2] = alpha1*(yz[1]-Ak[1]*mu[1])+(1-alpha1)*betak[1]
mu[2] = (TgtY[2] - betak[2])/Ak[2]

In [29]:
for k in range(2,599):
yz[k] =  ARRk[k]*mu[k]+beta0+results_AR.fittedvalues[k] # process output how to filter out
if(k%25)==1:
y[k] =  yz[k]
alpha = alpha1
else:
y[k] =  yz[k]*Virtual[k]
alpha = (1-RI[k])*alpha1
PostYv[k] = PreY[k] -  yz[k]*Virtual[k]
if PostYv[k]>2950 or PostYv[k]<2650:
alpha = 0
betak[k+1] = alpha*(y[k]-Ak[k]*mu[k])+(1-alpha)*betak[k]  #Toub canceled out
mu[k+1] = (TgtY[k+1] - betak[k+1])/Ak[k+1]
PostY[k] = PreY[k] -  yz[k] # Process output

In [30]:
x = range(2, 590)
plt.plot(x,betak[2:590])

In [31]:
x = range(10, 590)
U = np.empty(580)
L = np.empty(580)
U.fill(2950)
L.fill(2650)
# plot(x, PostY[3:600], type="o")
plt.plot(x,U)
plt.plot(x,L)
plt.plot(x,PostY[10:590])
np.mean(PostY[10:590])
plt.ylim([2600, 3000])

(2600, 3000)
In [32]:
Cpk=np.zeros(len(PreY))
MAPEp =np.zeros(len(PreY))

In [33]:
for k in range(2,599):
Cpk[k] = min((UCL-np.mean(PostY[1:k]))/(3*np.std(PostY[1:k], dtype=np.float64)), (np.mean(PostY[1:k])-LCL)/(3*np.std(PostY[1:k], dtype=np.float64)))
plt.plot(x,Cpk[10:590])
plt.ylim([0.3, 2.0])

(0.3, 2.0)
In [34]:
for k in range(2,599):
MAPEp[k] = sum(np.absolute((PostY[1:k]-2800)/2800))/k*100
plt.plot(x,MAPEp[10:590])
plt.ylim([0.7, 2.5])

(0.7, 2.5)

### CASE5: R2R+VM with RI.S.(1-RI)

In [35]:
# Reinitialized
alpha1 = 0.35
betak = np.zeros(len(PreY))
mu = np.zeros(len(PreY))
PostY = np.zeros(len(PreY))
PostYv = np.zeros(len(PreY))
y = np.zeros(len(PreY))
yz = np.zeros(len(PreY))

# Round1
mu[0] = (TgtY[0] - betak0)/Ak[0]
yz[0] = ARRk[0]*mu[0]+beta0+results_AR.fittedvalues[0]  # Actual value
PostY[0] = PreY[0] - yz[0]
betak[1] = alpha1*(yz[0]-Ak[0]*mu[0])+(1-alpha1)*betak0
mu[1] = (TgtY[1] - betak[1])/Ak[1]
# Round2
yz[1] = ARRk[1]*mu[1]+beta0+results_AR.fittedvalues[1]  # Actual value
PostY[1] = PreY[1] - yz[1]
betak[2] = alpha1*(yz[1]-Ak[1]*mu[1])+(1-alpha1)*betak[1]
mu[2] = (TgtY[2] - betak[2])/Ak[2]

In [36]:
for k in range(2,599):
yz[k] = ARRk[k]*mu[k]+beta0+results_AR.fittedvalues[k]
PostYv[k] = PreY[k]- yz[k]*Virtual[k]
if k>= 25:
if PostYv[k]>2950 or PostYv[k]<2650: # should be replace with RI, GSI
alpha = 0
else:
alpha = (1-RI[k])*alpha1
else:
if PostYv[k]>2950 or PostYv[k]<2650:
alpha = 0
else:
alpha = RI[k]*alpha1
if(k%25)==1:
y[k] = yz[k]
alpha = alpha1
else:
y[k] = yz[k]*Virtual[k]

betak[k+1] = alpha*(y[k]-Ak[k]*mu[k])+(1-alpha)*betak[k]  #Toub canceled out
mu[k+1] = (TgtY[k+1] - betak[k+1])/Ak[k+1]
PostY[k] = PreY[k] - yz[k]  # Process output

In [37]:
x = range(2, 590)
plt.plot(x,betak[2:590])

In [38]:
x = range(10, 590)
U = np.empty(580)
L = np.empty(580)
U.fill(2950)
L.fill(2650)
# plot(x, PostY[3:600], type="o")
plt.plot(x,U)
plt.plot(x,L)
plt.plot(x,PostY[10:590])
np.mean(PostY[10:590])
plt.ylim([2600, 3000])

(2600, 3000)
In [39]:
Cpk=np.zeros(len(PreY))
MAPEp =np.zeros(len(PreY))

In [40]:
for k in range(2,599):
Cpk[k] = min((UCL-np.mean(PostY[1:k]))/(3*np.std(PostY[1:k], dtype=np.float64)), (np.mean(PostY[1:k])-LCL)/(3*np.std(PostY[1:k], dtype=np.float64)))
plt.plot(x,Cpk[10:590])
plt.ylim([0.3, 2.0])

(0.3, 2.0)
In [41]:
for k in range(2,599):
MAPEp[k] = sum(np.absolute((PostY[1:k]-2800)/2800))/k*100
plt.plot(x,MAPEp[10:590])
plt.ylim([0.7, 2.5])

(0.7, 2.5)
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