CN110008551B - Resistance model and extraction method thereof - Google Patents
Resistance model and extraction method thereof Download PDFInfo
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Abstract
The invention relates to a resistor model, which relates to a semiconductor integrated circuit, wherein characteristic factors of a resistor width W and a resistor square number sqr which influence the voltage coefficient of a resistor are introduced into the resistor model to optimize the precision of the resistor model, and an artificial intelligence algorithm is adopted to optimize the characteristic factors of the resistor width W and the resistor square number sqr which influence the voltage coefficient of the resistor until the error convergence obtained according to the resistor model which takes the optimized resistor width adjusting factor and the resistor square number adjusting factor as the resistor width adjusting factor and the resistor square number adjusting factor meets the requirement, the optimized resistor width adjusting factor and the resistor square number adjusting factor are output, and the optimized resistor width adjusting factor and the optimized resistor square number adjusting factor are taken as the resistor width adjusting factor and the resistor square number adjusting factor of the resistor model, so that the novel resistor model can better and more accurately reflect the characteristics of a resistor device.
Description
Technical Field
The present invention relates to a semiconductor integrated circuit, and more particularly, to a resistor model and an extraction method thereof.
Background
In semiconductor integrated circuits, resistance is an important passive device in logic and analog circuits. The resistance in CMOS integrated circuits can be generally classified into Diffusion (Diffusion) resistance, POLY (POLY) resistance, well (NWELL) resistance, and metal resistance according to their processing processes and semiconductor materials. The resistivity and the precision of the resistors are different, and the resistors can be applied to different occasions. SPICE models typically use a macro model to fit the resistances. The macro model determines the characteristics of variable structure and flexible parameters of the resistance SPICE model. Custom parameters can be freely added to fit various characteristics of the resistor using the macro model. With the continuous advancement of process nodes, circuit design engineers continuously put new demands on the reliability and accuracy of the resistance model. However, current resistance models do not fully and precisely characterize the parameters of the resistance.
Disclosure of Invention
The invention aims to provide a resistance model which can better and more accurately reflect the characteristics of a resistance device.
The resistance model provided by the invention is as follows:
R＝Rsh0*(1+(JC1a+JC1a_w*W+JC1a_n*sqr+V*(JC1b+JC1b_w*W+JC1b_n*sqr)/L)/L+V*V*(JC2a+JC2a_w*W+JC2a_n*sqr+(JC2b+JC2b_w*W+JC2b_n*sqr)/L)/L)*L/W
where L is the length (cm) of the resistor, W is the cross-sectional area (square cm) of the resistor, V is the voltage (V) applied to the resistor, sqr is the square number, rsh0 is the resistance value when the voltage is zero, JC1a and JC1b are the length characteristic coefficients of the first voltage coefficient VC1, JC2a and JC2b are the length characteristic coefficients of the second voltage coefficient VC2, jc1a_w, jc1b_w, jc2a_w, jc2b_w are the resistance width adjustment factors, jc1a_n, jc1b_n, jc2a_n, jc2b_n are the resistance square number adjustment factors, where VC1 (1/L) =jc1a (1/L) +jc1b (1/L), and VC2 (1/L) =jc2a (1/L) +jc2b (1/L).
The invention also provides an extraction method of the resistance model, which comprises the following steps: s1: respectively measuring the resistances with different sizes to obtain resistance-voltage data, and calculating a resistance value Rsh0 when the voltage is zero; s2: normalizing the resistor to obtain a normalized resistance value, plotting the normalized resistance value and the voltage, wherein the X axis is the voltage, the Y axis is the normalized resistance value, obtaining a quadratic function of the resistance voltage, obtaining a first voltage coefficient VC1 and a second voltage coefficient VC2 through quadratic function fitting of the resistance voltage, and obtaining a group of first voltage coefficients VC1 (1-n) corresponding to different sizes and a group of second voltage coefficients VC2 (1-n) corresponding to different sizes through quadratic fitting of the resistance voltages with different sizes; s3: the first voltage coefficient VC1 is plotted with the reciprocal of the resistance length L, quadratic curve fitting is performed according to the trend of the graph, length characteristic coefficients JC1a and JC1b of the first voltage coefficient VC1 are obtained, and the first voltage coefficient VC1 is expressed by the following formulas of the length characteristic coefficients JC1a and JC1 b: VC1 (1/L) =jc1a (1/L) +jc1b (1/L); the second voltage coefficient VC2 is plotted with the reciprocal of the resistance length L, quadratic curve fitting is performed according to the trend of the graph, length characteristic coefficients JC2a and JC2b of the second voltage coefficient VC2 are obtained, and the second voltage coefficient VC2 is expressed by the following formulas of the length characteristic coefficients JC2a and JC2 b: VC2 (1/L) =jc2a (1/L) +jc2b (1/L); s4: decomposing length characteristic coefficients JC1a, JC1b, JC2a and JC2b, and introducing a resistance width W and square number sqr affecting the length characteristic coefficients into a resistance model as characteristic factors to obtain the resistance model: r=rsh0 (1+ (jc1a+jc1a_w+w+jc1a_n) sqr+v (jc1b+jc1b_w+jc1b_n_sqr)/L)/l+v (jc2a+jc2a_w+jc2a_n+sqr+ (jc2b+jc2b_w+jc2b_n_w)/L) L/W, wherein L is the length (cm) of the resistor, W is the cross-sectional area (square cm) of the resistor, V is the voltage (V) applied to the resistor, sqr is the number of blocks, rsh0 is the resistance value when the voltage is zero, jc1a and jc1b are the length characteristic coefficients of the first voltage coefficient VC1, jc2a and jc2b are the length characteristic coefficients of the second voltage coefficient VC2, jc2a_w+jc2b_n is the length (jc2b_w+jc2b_n) W/L), wherein W is the voltage (square centimeter) applied to the resistor, V is the voltage (V) at the voltage applied to the resistor, jc1b_1 b_n_n is the voltage (V) of the resistor, jc1b_n_1 b_n is the length (jc1b_n) of the resistor; VC2 (1/L) =jc2a (1/L) +jc2b (1/L); s5: and adjusting the resistance width adjustment factors JC1a_w, JC1b_w, JC2a_w and JC2b_w and the resistance square number adjustment factors JC1a_n, JC1b_n, JC2a_n and JC2b_n in the resistance model by adopting an artificial intelligent algorithm to obtain the optimized resistance width adjustment factors and resistance square number adjustment factors until the error convergence obtained according to the resistance model taking the optimized resistance width adjustment factors and resistance square number adjustment factors as the resistance width adjustment factors and resistance square number adjustment factors meets the requirements, outputting the optimized resistance width adjustment factors and resistance square number adjustment factors, and taking the optimized resistance width adjustment factors and resistance square number adjustment factors as the resistance width adjustment factors and resistance square number adjustment factors of the resistance model.
Further, step S5 more specifically includes: s51: performing resistance value simulation calculation by using the resistance model in the step S4, comparing the calculated value with measured resistance data to obtain a resistance error value, and entering the step S52; s52: judging whether the resistance error is converged to meet the requirement, if so, entering a step S53, and if not, entering a step S54; s53: outputting a resistor width adjustment factor and a resistor square number adjustment factor, and taking the resistor width adjustment factor and the resistor square number adjustment factor as a resistor width adjustment factor and a resistor square number adjustment factor of a resistor model to finish optimization; s54: performing a resistance width adjustment factor adjustment to produce an optimized resistance width adjustment factor; performing resistance square number adjustment factor adjustment to generate an optimized resistance square number adjustment factor, using the optimized resistance width adjustment factor and the optimized resistance square number adjustment factor as a resistance width adjustment factor and a resistance square number adjustment factor of the resistance model, and proceeding to step S55; s55: and (3) performing resistance value simulation calculation according to the resistance model obtained in the step S54, comparing the calculated value with measured resistance data to obtain a resistance error value, and proceeding to the step S52.
Still further, the condition satisfied by the convergence decreases as the number of optimizations of the artificial intelligence algorithm increases.
Still further, the number of optimizations for the artificial intelligence algorithm is between 100 and 1000.
Further, the weights of the resistor width adjustment factor and the resistor square number adjustment factor are set according to the physical meanings of the resistor width adjustment factor and the resistor square number adjustment factor.
Further, the weights of the resistance width adjustment factor and the resistance square adjustment factor are set according to the influence of the resistance width adjustment factor and the resistance square adjustment factor on the resistance value obtained according to the resistance model.
Still further, the artificial intelligence algorithm is a genetic algorithm.
Further, the normalization operation is to divide the resistance value at different voltages by the resistance value Rsh0 when the voltage is zero.
Further, the first voltage coefficient VC1 is a parameter affecting the curvature bending direction of the quadratic function curve.
Further, the second voltage coefficient VC2 is a parameter that affects the degree of curvature bending of the quadratic function curve.
Further, step S1 is to design the resistor device, and make the length L of the resistor body of the resistor device and the cross-sectional area W of the resistor body different, and measure the resistance value of the resistor under different voltages applied to the resistors of different sizes.
Further, the resistance value of the resistor was measured under different voltages applied to resistors of different sizes at room temperature of 25 ℃.
According to the resistance model provided by the invention, the characteristic factors of the resistance width W and the resistance square number sqr which influence the voltage coefficient of the resistor are introduced into the resistance model to optimize the precision of the resistance model, and the characteristic factors of the resistance width W and the resistance square number sqr which influence the voltage coefficient of the resistor are optimized by adopting an artificial intelligence algorithm until the error convergence obtained according to the resistance model which takes the optimized resistance width adjusting factor and the optimized resistance square number adjusting factor as the resistance width adjusting factor and the optimized resistance square number adjusting factor meets the requirement, the optimized resistance width adjusting factor and the optimized resistance square number adjusting factor are output, and the optimized resistance width adjusting factor and the optimized resistance square number adjusting factor are taken as the resistance width adjusting factor and the resistance square number adjusting factor of the resistance model, so that the novel resistance model can better and more accurately reflect the characteristics of a resistance device.
Drawings
Fig. 1 is a schematic diagram of resistance (normalization) versus voltage fitting in a voltage parameter extraction mode in the prior art.
Fig. 2 is a schematic diagram of fitting resistance (normalization) to voltage in a voltage parameter extraction mode according to an embodiment of the invention.
FIG. 3 is a schematic diagram showing the fitting of the first voltage coefficient VC1 to 1/L.
FIG. 4 is a schematic diagram showing the fitting of the second voltage coefficient VC2 to 1/L.
FIG. 5 is a flow chart of an artificial intelligence algorithm according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The resistance of the resistor element is generally related to the temperature, material, length, and cross-sectional area of the resistor body, and the resistance of the resistor element is more precisely related to the voltage V applied across the resistor, and the current commonly used resistor model can be referred to as formula 1:
r=rsh0 (1+V (jc1a+jc1b/L)/l+v (jc2a+jc2b/L) formula 1
Wherein L is the length (cm) of the resistor, V is the voltage (V) applied across the resistor, JC1a and JC1b are the length characteristic coefficients of the first voltage coefficient VC1, JC2a and JC2b are the length characteristic coefficients of the second voltage coefficient VC2, rsh0 is the resistance value when the voltage is zero, wherein VC1 (1/L) =jc1a (1/L) +jc1b (1/L); VC2 (1/L) =jc2a (1/L) +jc2b (1/L) x (1/L).
The resistor voltage extraction method in the resistor model is to obtain a group of first voltage coefficient VC1 and second voltage coefficient VC2 based on resistors measured by resistors with different sizes, and then obtain length characteristic coefficients JC1a, JC1b, JC2a, JC2b related to the resistor length based on the voltage coefficients. The voltage parameter obtained by the method ignores the influence of the resistor width W and the resistor square number sqr on the resistor value, and tends to cause larger errors between the simulation value and the actual measurement value of the resistor model, namely the reliability and the accuracy of the resistor model are lower.
In an embodiment of the invention, a resistor model is provided, and the resistor model introduces characteristic factors of the resistor width W and the resistor square number sqr which affect the voltage coefficient of a resistor into the resistor model to optimize the precision of the resistor model, so that the novel resistor model can better and more accurately reflect the characteristics of a resistor device, and provides great help for a designer to design a more reasonable resistor layout in circuit design.
The resistance model provided by the invention is shown as formula 2:
r=rsh0 (1+ (jc1a+jc1a_w+w+jc1a_n_sqr+v (jc1b+jc1b_w+jc1b_n_sqr)/L)/l+v (jc2a+jc2a_w+w+jc2a_n_sqr+ (jc2b+jc2b_w+jc2b_n_sqr)/L) L/W formula 2
Where L is the length (cm) of the resistor, W is the cross-sectional area (square cm) of the resistor, V is the voltage (V) applied to the resistor, sqr is the square number, rsh0 is the resistance value when the voltage is zero, JC1a and JC1b are the length characteristic coefficients of the first voltage coefficient VC1, JC2a and JC2b are the length characteristic coefficients of the second voltage coefficient VC2, jc1a_w, jc1b_w, jc2a_w, jc2b_w are the resistance width adjustment factors, jc1a_n, jc1b_n, jc2a_n, jc2b_n are the resistance square number adjustment factors, where VC1 (1/L) =jc1a (1/L) +jc1b (1/L), and VC2 (1/L) =jc2a (1/L) +jc2b (1/L).
In an embodiment of the present invention, there is further provided a method for extracting the above resistance model, including:
s1: resistance-voltage data are obtained by measuring the resistances of different sizes respectively, and the resistance value Rsh0 when the voltage is zero is calculated.
Specifically, in one embodiment of the present invention, the resistor device is designed, and the length L of the resistor body of the resistor device and the cross-sectional area W of the resistor body are made different, and the resistance value of the resistor is measured under different voltages applied to resistors of different sizes. More specifically, the resistance value of the resistor was measured under different voltages applied to resistors of different sizes at room temperature of 25 ℃.
S2: normalizing the resistor to obtain a normalized resistance value, plotting the normalized resistance value and the voltage, wherein the X axis is the voltage, the Y axis is the normalized resistance value, obtaining a quadratic function of the resistance voltage, obtaining a first voltage coefficient VC1 and a second voltage coefficient VC2 through quadratic function fitting of the resistance voltage, and obtaining a group of first voltage coefficients VC1 (1-n) corresponding to different sizes and a group of second voltage coefficients VC2 (1-n) corresponding to different sizes through quadratic fitting of the resistance voltages with different sizes.
Wherein the normalization operation is to divide the resistance value at different voltages by the resistance value Rsh0 when the voltage is zero. The first voltage coefficient VC1 is a parameter affecting the curvature bending direction of the quadratic function curve, and the second voltage coefficient VC2 is a parameter affecting the curvature bending degree of the quadratic function curve.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic diagram of fitting resistance (normalization) to voltage in a voltage parameter extraction mode in the prior art, and fig. 2 is a schematic diagram of fitting resistance (normalization) to voltage in a voltage parameter extraction mode according to an embodiment of the present invention, wherein dots represent actual measurement data, and cross lines represent simulation values of a simulation model. As shown in fig. 1 and 2, the resistance voltage accords with the characteristic of a quadratic function under normal conditions, and the fitting precision of the voltage-resistance curve is remarkably improved by adopting the method disclosed by the invention as shown in fig. 2.
S3: the first voltage coefficient VC1 is plotted with the reciprocal of the resistance length L, quadratic curve fitting is performed according to the trend of the graph, length characteristic coefficients JC1a and JC1b of the first voltage coefficient VC1 are obtained, and the first voltage coefficient VC1 is expressed by the following formulas of the length characteristic coefficients JC1a and JC1 b: VC1 (1/L) =jc1a (1/L) +jc1b (1/L); the second voltage coefficient VC2 is plotted with the reciprocal of the resistance length L, quadratic curve fitting is performed according to the trend of the graph, length characteristic coefficients JC2a and JC2b of the second voltage coefficient VC2 are obtained, and the second voltage coefficient VC2 is expressed by the following formulas of the length characteristic coefficients JC2a and JC2 b: VC2 (1/L) =jc2a (1/L) +jc2b (1/L) x (1/L).
Specifically, referring to fig. 3 and 4, fig. 3 is a schematic fitting diagram of the first voltage coefficient VC1 to 1/L, and fig. 4 is a schematic fitting diagram of the second voltage coefficient VC2 to 1/L, wherein the dots are actual data; the line is a quadratic fit curve.
S4: decomposing length characteristic coefficients JC1a, JC1b, JC2a and JC2b, and introducing a resistance width W and square number sqr affecting the length characteristic coefficients into a resistance model as characteristic factors to obtain the resistance model: r=rsh0 (1+ (jc1a+jc1a_w+w+jc1a_n) sqr+v (jc1b+jc1b_w+jc1b_n_sqr)/L)/l+v (jc2a+jc2a_w+jc2a_n+sqr+ (jc2b+jc2b_w+jc2b_n_w)/L) L/W, wherein L is the length (cm) of the resistor, W is the cross-sectional area (square cm) of the resistor, V is the voltage (V) applied to the resistor, sqr is the number of blocks, rsh0 is the resistance value when the voltage is zero, jc1a and jc1b are the length characteristic coefficients of the first voltage coefficient VC1, jc2a and jc2b are the length characteristic coefficients of the second voltage coefficient VC2, jc2a_w+jc2b_n is the length (jc2b_w+jc2b_n) W/L), wherein W is the voltage (square centimeter) applied to the resistor, V is the voltage (V) at the voltage applied to the resistor, jc1b_1 b_n_n is the voltage (V) of the resistor, jc1b_n_1 b_n is the length (jc1b_n) of the resistor; VC2 (1/L) =jc2a (1/L) +jc2b (1/L) x (1/L).
S5: and adjusting the resistance width adjustment factors JC1a_w, JC1b_w, JC2a_w and JC2b_w and the resistance square number adjustment factors JC1a_n, JC1b_n, JC2a_n and JC2b_n in the resistance model by adopting an artificial intelligent algorithm to obtain the optimized resistance width adjustment factors and resistance square number adjustment factors until the error convergence obtained according to the resistance model taking the optimized resistance width adjustment factors and resistance square number adjustment factors as the resistance width adjustment factors and resistance square number adjustment factors meets the requirements, outputting the optimized resistance width adjustment factors and resistance square number adjustment factors, and taking the optimized resistance width adjustment factors and resistance square number adjustment factors as the resistance width adjustment factors and resistance square number adjustment factors of the resistance model.
Specifically, step S5 is a process of optimizing the resistance width adjustment factors jc1a_w, jc1b_w, jc2a_w, jc2b_w, and the resistance square number adjustment factors jc1a_n, jc1b_n, jc2a_n, jc2b_n by using an artificial intelligence algorithm, and referring to fig. 5, fig. 5 is a flowchart of an artificial intelligence algorithm according to an embodiment of the invention, and step S5 further includes:
s51: performing resistance value simulation calculation by using the resistance model in the step S4, comparing the calculated value with measured resistance data to obtain a resistance error value, and entering the step S52;
s52: judging whether the resistance error is converged to meet the requirement, if so, entering a step S53, and if not, entering a step S54;
s53: outputting a resistor width adjustment factor and a resistor square number adjustment factor, and taking the resistor width adjustment factor and the resistor square number adjustment factor as a resistor width adjustment factor and a resistor square number adjustment factor of a resistor model to finish optimization;
s54: performing a resistance width adjustment factor adjustment to produce an optimized resistance width adjustment factor; performing resistance square number adjustment factor adjustment to generate an optimized resistance square number adjustment factor, using the optimized resistance width adjustment factor and the optimized resistance square number adjustment factor as a resistance width adjustment factor and a resistance square number adjustment factor of the resistance model, and proceeding to step S55;
s55: and (3) performing resistance value simulation calculation according to the resistance model obtained in the step S54, comparing the calculated value with measured resistance data to obtain a resistance error value, and proceeding to the step S52.
In an embodiment of the present invention, the condition satisfied by the convergence decreases with increasing optimization times of the artificial intelligence algorithm, so that the resistance width adjustment factor and the resistance square number adjustment factor of the resistance model approach the optimal values.
In one embodiment of the invention, the optimization time of the artificial intelligence algorithm is between 100 times and 1000 times, so that the optimization time is proper, useless multiple loops are avoided, and the expected resistance width adjustment factor and resistance square number adjustment factor are obtained.
In one embodiment of the present invention, the weights of the resistor width adjustment factor and the resistor square number adjustment factor are set according to the physical meanings of the resistor width adjustment factor and the resistor square number adjustment factor, for example, the physical meaning of the resistor width adjustment factor is related to the resistor width, and the physical meaning of the resistor square number adjustment factor is related to the resistor square number.
In an embodiment of the present invention, weights of the resistance width adjustment factor and the resistance square adjustment factor are set according to an influence of the resistance width adjustment factor and the resistance square adjustment factor on a resistance value obtained according to the resistance model, if the influence of the resistance width adjustment factor on a resistance obtained according to the resistance model is large, a larger weight is set.
In an embodiment of the invention, the artificial intelligence algorithm is a genetic algorithm, and a better resistance model can be obtained in a shorter time by adopting the genetic algorithm. Of course, the invention can also be used with other artificial intelligence algorithms, such as fuzzy operations.
In summary, the characteristic factors of the resistor width W and the resistor square number sqr affecting the voltage coefficient of the resistor are introduced into the resistor model to optimize the precision of the resistor model, and the characteristic factors of the resistor width W and the resistor square number sqr affecting the voltage coefficient of the resistor are optimized by adopting an artificial intelligence algorithm until the error convergence obtained according to the resistor model with the optimized resistor width adjusting factor and the optimized resistor square number adjusting factor as the resistor width adjusting factor and the resistor square number adjusting factor meets the requirement, the optimized resistor width adjusting factor and the optimized resistor square number adjusting factor are output, and the optimized resistor width adjusting factor and the optimized resistor square number adjusting factor are used as the resistor width adjusting factor and the resistor square number adjusting factor of the resistor model, so that the novel resistor model can better and more accurately reflect the characteristics of the resistor device.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (11)
1. The method for extracting the resistance model is characterized by comprising the following steps of:
s1: respectively measuring the resistances with different sizes to obtain resistance-voltage data, and calculating a resistance value Rsh0 when the voltage is zero;
s2: normalizing the resistor to obtain a normalized resistance value, plotting the normalized resistance value and the voltage, wherein the X axis is the voltage, the Y axis is the normalized resistance value, obtaining a quadratic function of the resistance voltage, obtaining a first voltage coefficient VC1 and a second voltage coefficient VC2 through quadratic function fitting of the resistance voltage, and obtaining a group of first voltage coefficients VC1 corresponding to different sizes and a group of second voltage coefficients VC2 corresponding to different sizes through quadratic fitting of the resistance voltages with different sizes;
s3: the first voltage coefficient VC1 is plotted with the reciprocal of the resistance length L, quadratic curve fitting is performed according to the trend of the graph, length characteristic coefficients JC1a and JC1b of the first voltage coefficient VC1 are obtained, and the first voltage coefficient VC1 is expressed by the following formulas of the length characteristic coefficients JC1a and JC1 b: VC1 (1/L) =jc1a (1/L) +jc1b (1/L); the second voltage coefficient VC2 is plotted with the reciprocal of the resistance length L, quadratic curve fitting is performed according to the trend of the graph, length characteristic coefficients JC2a and JC2b of the second voltage coefficient VC2 are obtained, and the second voltage coefficient VC2 is expressed by the following formulas of the length characteristic coefficients JC2a and JC2 b: VC2 (1/L) =jc2a (1/L) +jc2b (1/L);
s4: decomposing length characteristic coefficients JC1a, JC1b, JC2a and JC2b, and introducing a resistance width W and square number sqr affecting the length characteristic coefficients into a resistance model as characteristic factors to obtain the resistance model: r=
Rsh0 (1+ (jc1a+jc1a_w+jc1a_n) sqr+v (jc1b+jc1b_w+jc1b_n_sqr)/L)/V (jc2a+jc2a_w+jc2a_n) sqr+ (jc2b+jc2b_w_w+jc2b_n_sqr)/L), wherein L is the length of the resistor, W is the cross-sectional area of the resistor, V is the voltage applied to the resistor, sqr is the number of blocks, rsh0 is the resistance value when the voltage is zero, jc1a and jc1b are the length characteristic coefficients of the first voltage coefficient VC1, jc2a and jc2b are the length characteristic coefficients of the second voltage coefficient VC2, jc1a_w, jc1b_w, jc2b_w_n is the adjustment factor, jc2b_n is the number of blocks, jc1b_1, jc2b_n is the adjustment factor, jc1b_n_1, jc1b_n is the number of blocks, jc1b_n_1; VC2 (1/L) =
JC2a (1/L) +jc2b (1/L); and
s5: and adjusting the resistance width adjustment factors JC1a_w, JC1b_w, JC2a_w and JC2b_w and the resistance square number adjustment factors JC1a_n, JC1b_n, JC2a_n and JC2b_n in the resistance model by adopting an artificial intelligent algorithm to obtain the optimized resistance width adjustment factors and resistance square number adjustment factors until the error convergence obtained according to the resistance model taking the optimized resistance width adjustment factors and resistance square number adjustment factors as the resistance width adjustment factors and resistance square number adjustment factors meets the requirements, outputting the optimized resistance width adjustment factors and resistance square number adjustment factors, and taking the optimized resistance width adjustment factors and resistance square number adjustment factors as the resistance width adjustment factors and resistance square number adjustment factors of the resistance model.
2. The method of extracting a resistance model according to claim 1, wherein step S5 more specifically includes:
s51: performing resistance value simulation calculation by using the resistance model in the step S4, comparing the calculated value with measured resistance data to obtain a resistance error value, and entering the step S52;
s52: judging whether the resistance error is converged to meet the requirement, if so, entering a step S53, and if not, entering a step S54;
s53: outputting a resistor width adjustment factor and a resistor square number adjustment factor, and taking the resistor width adjustment factor and the resistor square number adjustment factor as a resistor width adjustment factor and a resistor square number adjustment factor of a resistor model to finish optimization;
s54: performing a resistance width adjustment factor adjustment to produce an optimized resistance width adjustment factor; performing resistance square number adjustment factor adjustment to generate an optimized resistance square number adjustment factor, using the optimized resistance width adjustment factor and the optimized resistance square number adjustment factor as a resistance width adjustment factor and a resistance square number adjustment factor of the resistance model, and proceeding to step S55; and
s55: and (3) performing resistance value simulation calculation according to the resistance model obtained in the step S54, comparing the calculated value with measured resistance data to obtain a resistance error value, and proceeding to the step S52.
3. The method for extracting a resistance model according to claim 1, wherein the number of optimizations of the artificial intelligence algorithm is between 100 times and 1000 times.
4. The method according to claim 1, wherein weights of the resistance width adjustment factor and the resistance square adjustment factor are set according to physical meanings of the resistance width adjustment factor and the resistance square adjustment factor.
5. The method according to claim 1 or 4, wherein the weights of the resistance width adjustment factor and the resistance square adjustment factor are set according to the influence of the resistance width adjustment factor and the resistance square adjustment factor on the resistance value obtained from the resistance model.
6. The method of claim 1, wherein the artificial intelligence algorithm is a genetic algorithm.
7. The method of claim 1, wherein the normalization is performed by dividing the resistance value at different voltages by the resistance value Rsh0 at zero.
8. The method of extracting a resistance model according to claim 1, wherein the first voltage coefficient VC1 is a parameter affecting a curvature bending direction of the quadratic function curve.
9. The method of extracting a resistance model according to claim 1, wherein the second voltage coefficient VC2 is a parameter affecting a degree of curvature bending of the quadratic function curve.
10. The method according to claim 1, wherein step S1 is to design a resistor device, and to make the length L of the resistor body of the resistor device and the cross-sectional area W of the resistor body different, and to measure the resistance value of the resistor under different voltages applied to resistors of different sizes.
11. The method according to claim 10, wherein the resistance values of the resistors are measured under different voltages applied to the resistors of different sizes at room temperature of 25 ℃.
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| CN112084740B (en) * | 2020-09-17 | 2022-06-21 | 南京华大九天科技有限公司 | Resistance model modeling method |
| CN112966387B (en) * | 2021-03-15 | 2024-02-20 | 上海华力微电子有限公司 | Modeling method for unit square resistance with fixed width |
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| CN116578155B (en) * | 2023-07-14 | 2023-09-15 | 上海英联电子科技有限公司 | Adjustment method and system for trimming circuit |
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