MODELING OF THIN FILM PROCESS DATA USING A GENETIC ALGORITHM-OPTIMIZED INITIAL WEIGHT OF BACKPROPAGATION NEURAL NETWORK

Figures & data

FIGURE 1 Schematic of inductively coupled plasma equipment.

FIGURE 2 Schematic of a backpropagation neural network.

FIGURE 3 Prediction performance of a silica etch rate model as a function of initial weight distribution.

TABLE 1 Prediction Errors at Optimized Initial Weight Distribution

Etch responses	Prediction error	Initial weight distribution
Silica etch rate (Å/min)	267	1.0
Al etch rate (Å/min)	432	1.0
Profile angle (Degree)	2.43	1.2
Al selectivity	1.88	1.0

FIGURE 4 Prediction performance of a silica etch rate model as a function of combinations of Pc and Pm.

FIGURE 5 Prediction performance of an Al etch rate model as a function of combinations of Pc and Pm.

FIGURE 6 Prediction performance of an anisotropy model as a function of combinations of Pc and Pm.

FIGURE 7 Prediction performance of an Al selectivity model as a function of combinations of Pc and Pm.

TABLE 2 Prediction Errors at Optimized Pc and Pm

Etch responses	Prediction error	(Pc, Pm)
Silica etch rate (Å./min)	202	(0.95, 0.15)
Al etch rate (Å./min)	391	(0.80, 0.20)
Profile angle (degree)	2.05	(0.90, 0.15)
Al selectivity	1.58	(0.95, 0.10)

FIGURE 8 Comparison of GA-BPNN and BPNN models.