Anomaly detection of semiconductor processing equipment using equipment behaviour

Figures & data

Figure 1. Schematic diagram of deposition equipment.

Figure 2. Scatter plots of oxygen concentration and pressure in the monitoring section: (a) normal equipment and (b) anomalous equipment.

Figure 3. Oxygen concentration change during N2 purge.

Table 1. Measurements and preprocessing used for feature extraction.

	OX	PR10MA,d	OX10MA,d	PR100MA,d	PC1
F01	*	*
F02	*	*
F03	*	*
F04	*	*
F05	*	*
F06	*
F07			*
F08				*
F09			*	*
F10			*	*
F11			*	*
F12					*
F13					*
F14					*
F15					*
F16				*

Figure 4. Image of displacement from the first quartile interval to the fourth quartile interval.

Table 2. Two-dimensional distribution table of OX and PR_10MA,d.

		OX
		3	4	5	6	7
PR10MA,d	49	0	0	0	0	0
	50	78	68	28	0	0
	51	57	59	52	61	51
	52	0	0	0	0	0

Table 3. Features calculated from data acquired from the equipment.

	F01	F02	F03	F04	F05	F06	F07	F08	F09	F10	F11	F12	F13	F14	F15	F16
E01	0.16	1.4	0.04	39.2	57	19.3	0.004	0.003	19	−22	7.9	−0.32	−0.31	1.46	1.42	4.2
E02	3.83	0.9	0.89	28.5	53	17.6	0.005	0.001	18	17.1	3.3	−0.16	−0.15	1.00	0.94	6.3
E03	2.36	4.4	0.12	19.8	47	11.2	0.005	0.004	3	−44	6.6	−0.28	−0.28	1.39	1.37	−0.1
E04	9.20	2.0	0.76	26.9	50	8.1	0.005	0.003	3	−28	4.5	−0.23	−0.20	1.46	1.26	−1.7
E05	7.31	4.3	0.44	11.0	50	10.0	0.005	0.002	6	−19	5.0	−0.17	−0.15	0.99	0.84	1.9
E06	9.13	2.2	0.90	13.6	51	11.4	0.005	0.004	15	−26	−2.7	0.01	0.01	0.84	0.81	−2.1
E07	4.84	1.4	0.92	22.5	50	7.1	0.005	0.003	20	−19	2.7	−0.16	−0.16	1.14	1.11	2.6
E08	7.71	2.1	0.84	19.4	53	14.7	0.007	0.001	18	−4.1	4.1	−0.19	−0.18	0.99	0.94	6.1
E09	10.10	3.2	0.83	16.2	56	10.3	0.005	0.004	15	−19	5.3	−0.04	−0.04	0.68	0.60	0.2
E10	8.26	2.9	0.67	35.7	53	7.3	0.005	0.003	20	−23	6.6	−0.25	−0.22	1.37	1.20	2.8
E11	5.59	1.7	0.90	17.4	46	8.9	0.005	0.003	20	−16	5.2	−0.22	−0.21	1.31	1.24	2.0
E12	7.47	2.8	0.66	20.1	52	5.7	0.004	0.006	19	−28	−0.3	−0.09	−0.08	0.96	0.86	−0.1
E13	5.70	2.9	0.78	15.7	46	12.4	0.006	0.004	15	−26	2.8	−0.22	−0.21	1.35	1.32	−1.5
E14	7.04	1.7	0.83	30.1	56	7.3	0.004	0.005	9	−38	6.0	−0.10	−0.09	1.02	0.91	0.4
E15	5.77	2.1	0.79	24.4	51	5.9	0.005	0.003	16	−18	3.9	−0.28	−0.26	1.50	1.37	0.8
E16	9.77	2.3	0.84	16.0	55	10.0	0.006	0.003	19	−35	1.7	−0.14	−0.12	1.05	0.89	1.6

Table 4. Comparison of AUC for univariate detection models.

feature	AUC
F06	0.93
F01	0.67
F09	0.67
F08	0.63
F16	0.61
F02	0.57
F03	0.57
F04	0.57
F14	0.57
F05	0.54
F15	0.53
F07	0.51
F12	0.50
F10	0.47
F13	0.47
F11	0.44

Table 5. Comparison of detection ability of models made with two features.

feature 1	feature 2	AUC
F06	F16	1.00
	F10	0.95
	F03	0.93
	F07	0.93
	F08	0.93
	F12	0.93
	F13	0.93
	F14	0.93
	F15	0.93
	F01	0.92
	F11	0.92
	F02	0.90
	F04	0.90
	F09	0.90
	F05	0.90

Table 6. Comparison of detection ability of models made with three features.

feature 1	feature 2	feature 3	AUC
F06	F10	F11	0.967
		F01	0.950
		F03	0.950
		F07	0.950
		F08	0.950
		F12	0.950
		F13	0.950
		F14	0.950
		F15	0.950
		F05	0.933
		F02	0.933
		F09	0.933
		F04	0.917

Table 7. Comparison of detection ability of models made with four features.

feature 1	feature 2	feature 3	feature 4	AUC
F06	F10	F11	F03	0.967
			F07	0.967
			F08	0.967
			F12	0.967
			F13	0.967
			F14	0.967
			F15	0.967

Table 8. Anomaly detection result by a single variable model (using F06).

	Detection result OK	Detection result NG	accuracy
Quality judgment OK	29	2	82%
Quality judgment NG	5	3

Table 9. Anomaly detection result by two variables model (using F06, F16).

	Detection result OK	Detection result NG	accuracy
Quality judgment OK	29	2	82%
Quality judgment NG	5	3

Table 10. Anomaly detection result by three variables model (using F06, F10, F11).

	Detection result OK	Detection result NG	accuracy
Quality judgment OK	29	2	87%
Quality judgment NG	3	5