Merge pull request google#79 from mabrains/xyce_regression

xyce add mos cv
proppy · Jan 22, 2023 · fa1f671 · fa1f671
2 parents bafca6b + c1557bf
commit fa1f671
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Showing 6 changed files with 26 additions and 67 deletions.
diff --git a/models/xyce/testing/regression/bjt_cj/device_netlists/run_npn_cv.spice b/models/xyce/testing/regression/bjt_cj/device_netlists/run_npn_cv.spice
diff --git a/models/xyce/testing/regression/bjt_cj/device_netlists/run_pnp_cv.spice b/models/xyce/testing/regression/bjt_cj/device_netlists/run_pnp_cv.spice
diff --git a/models/xyce/testing/regression/mos_cv/device_netlists_Cgc/mos.spice b/models/xyce/testing/regression/mos_cv/device_netlists_Cgc/mos.spice
@@ -25,10 +25,11 @@ xmn1 0 G_tn 0 S_tn {{device}} W = {{width}}u L = {{length}}u nf={{nf}} ad={{AD}}
 *****************
 ** Analysis
 *****************
-.DC Vgs {{vgs}} Vbs {{vbs}}
+.TRAN 0 0.0001ms
+.STEP Vgs {{vgs}} 
+.STEP Vbs {{vbs}}
 .STEP TEMP 25 -60 200
-.print DC FORMAT=CSV file=mos_cv_regr/{{device}}/{{device}}_netlists_Cgc/simulated_W{{width}}_L{{length}}.csv {-1.0e15*N(xmn1:m0:cgs)} v(S_tn) v(G_tn) 
-
+.print tran FORMAT=CSV file=mos_cv_regr/{{device}}/{{device}}_netlists_Cgc/simulated_W{{width}}_L{{length}}.csv {-1.0e15*N(xmn1:m0:cgs) - 1.0e15*N(xmn1:m0:cgd)} v(S_tn) v(G_tn) 
 .include "../../../design.xyce"
 .lib "../../../sm141064.xyce" typical
 .end

diff --git a/models/xyce/testing/regression/mos_cv/device_netlists_Cgd/mos.spice b/models/xyce/testing/regression/mos_cv/device_netlists_Cgd/mos.spice
@@ -24,9 +24,11 @@ xmn1 D_tn G_tn 0 0 {{device}} W = {{width}}u L = {{length}}u nf={{nf}} ad={{AD}}
 *****************
 ** Analysis
 *****************
-.DC Vds {{vds}} Vgs {{vgs}}
+.TRAN 0 0.0001ms
+.STEP Vds {{vds}} 
+.STEP Vgs {{vgs}}
 .STEP TEMP 25 -60 200
-.print DC FORMAT=CSV file=mos_cv_regr/{{device}}/{{device}}_netlists_Cgd/simulated_W{{width}}_L{{length}}.csv {-1.0e15*N(xmn1:m0:cgd)} v(G_tn) v(D_tn)
+.print tran FORMAT=CSV file=mos_cv_regr/{{device}}/{{device}}_netlists_Cgd/simulated_W{{width}}_L{{length}}.csv {-1.0e15*N(xmn1:m0:cgd)} v(G_tn) v(D_tn)
 
 .include "../../../design.xyce"
 .lib "../../../sm141064.xyce" typical

diff --git a/models/xyce/testing/regression/mos_cv/device_netlists_Cgs/mos.spice b/models/xyce/testing/regression/mos_cv/device_netlists_Cgs/mos.spice
@@ -24,9 +24,11 @@ xmn1 D_tn G_tn 0 0 {{device}} W = {{width}}u L = {{length}}u nf={{nf}} ad={{AD}}
 *****************
 ** Analysis
 *****************
-.DC Vds {{vds}} Vgs {{vgs}}
+.TRAN 0 0.0001ms
+.STEP Vds {{vds}} 
+.STEP Vgs {{vgs}}
 .STEP TEMP 25 -60 200
-.print DC FORMAT=CSV file=mos_cv_regr/{{device}}/{{device}}_netlists_Cgs/simulated_W{{width}}_L{{length}}.csv {-1.0e15*N(xmn1:m0:cgs)} v(D_tn) v(G_tn)
+.print tran FORMAT=CSV file=mos_cv_regr/{{device}}/{{device}}_netlists_Cgs/simulated_W{{width}}_L{{length}}.csv {-1.0e15*N(xmn1:m0:cgs)} v(D_tn) v(G_tn)
 
 .include "../../../design.xyce"
 .lib "../../../sm141064.xyce" typical

diff --git a/models/xyce/testing/regression/mos_cv/models_regression.py b/models/xyce/testing/regression/mos_cv/models_regression.py
@@ -379,8 +379,6 @@ def run_sim(dirpath: str, device: str, width: float, length: float, nf: float) -
             vgs = vgsd
             vds = vdsd
             vbs = vbsc
-        if width_str == 200:
-            width_str = 100
 
         s = f"netlist_w{width_str}_l{length_str}.spice"
         netlist_path = f"{dirpath}/{device}_netlists_Cg{cap}/{s}"
@@ -489,12 +487,16 @@ def run_sims(df: pd.DataFrame, dirpath: str, device: str, num_workers=mp.cpu_cou
         # sweeping on all generated cvs files
         for i in range(len(sf)):
             df = pd.read_csv(sf[i])
-
+ 
             if cap == "c":
                 # use the first column as index
                 v_s = "V(S_TN)"
-                i_vds = "{-1.0E15*N(XMN1:M0:CGS)}"
+                i_vds = "{-1.0E15*N(XMN1:M0:CGS) - 1.0E15*N(XMN1:M0:CGD)}"
                 v1 = "V(G_TN)"
+                # drop time column
+                df.drop(df.columns[0], axis=1, inplace=True)
+                # drop duplicate rows for columns V(S_TN) and V(G_TN)
+                df.drop_duplicates(subset=["V(S_TN)", "V(G_TN)"], inplace=True)
                 sdf = df.pivot(index=v1, columns=(v_s), values=i_vds)
                 # Writing final simulated data 1
                 sdf.rename(
@@ -514,6 +516,10 @@ def run_sims(df: pd.DataFrame, dirpath: str, device: str, num_workers=mp.cpu_cou
                         inplace=True,
                     )
             elif cap == "d":
+                # drop time column
+                df.drop(df.columns[0], axis=1, inplace=True)
+                # drop duplicate rows for columns V(S_TN) and V(G_TN)
+                df.drop_duplicates(subset=["V(D_TN)", "V(G_TN)"], inplace=True)                
                 # use the first column as index
                 v_s = "V(G_TN)"
                 i_vds = "{-1.0E15*N(XMN1:M0:CGD)}"
@@ -526,6 +532,10 @@ def run_sims(df: pd.DataFrame, dirpath: str, device: str, num_workers=mp.cpu_cou
                     inplace=True,
                 )
             else:
+                # drop time column
+                df.drop(df.columns[0], axis=1, inplace=True)
+                # drop duplicate rows for columns V(S_TN) and V(G_TN)
+                df.drop_duplicates(subset=["V(D_TN)", "V(G_TN)"], inplace=True)                
                 # use the first column as index
                 v_s = "V(G_TN)"
                 i_vds = "{-1.0E15*N(XMN1:M0:CGS)}"