CN107480331A - Modeling method and device of semiconductor device statistical model - Google Patents
Modeling method and device of semiconductor device statistical model Download PDFInfo
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Abstract
The invention provides a modeling method and a device of a semiconductor device statistical model, wherein the method comprises the following steps: sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module; establishing a circuit model of the semiconductor device; arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form a semiconductor device statistical model; therefore, when the statistical model of the semiconductor device is established, the process corner module is combined, and the influence of the process corner on the performance of the semiconductor device is considered, so that the analysis precision is higher when the established process model of the semiconductor device analyzes the influence of the process fluctuation on the performance of the device, and the precision of the semiconductor device can be further improved.
Description
Technical Field
The invention belongs to the technical field of semiconductor device modeling, and particularly relates to a modeling method and device of a semiconductor device statistical model.
Background
With the development and wider application of integrated circuit technology, the requirements of high reliability, high performance and low cost must be considered when designing integrated circuits.
Various process fluctuations exist in various steps in the integrated circuit manufacturing process, and the process fluctuations can cause the performance of devices including threshold voltage, saturation current and the like to fluctuate, thereby affecting the product yield. The smaller the device feature size, the greater the impact of such fluctuations on the devices and circuits.
The influence of the process fluctuation on the device performance can be represented by a statistical model of the device, and when the statistical model in the prior art is used for counting the influence of the process fluctuation on the device performance, certain difficulty exists when the statistical model is combined with a process angle model, so that the analysis accuracy is not high when the model is used for analyzing the influence of the process fluctuation on the device performance.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a modeling method and a device of a semiconductor device statistical model, which are used for solving the technical problem that when the influence of process fluctuation on the device performance is analyzed by using the device statistical model in the prior art, the analysis precision is not high when the influence of the process fluctuation on the device performance is analyzed by the statistical model and a process angle model due to certain difficulty in combination.
The invention provides a modeling method of a semiconductor device statistical model, which comprises the following steps:
sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module;
establishing a circuit model of the semiconductor device;
and arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form the semiconductor device statistical model.
In the foregoing solution, the arranging the process corner module, the global fluctuation module, the local fluctuation module, and the circuit model based on a preset arrangement rule includes:
and based on a preset arrangement rule, the process corner module and the global fluctuation module are arranged outside the initial running identifier or the ending running identifier of the circuit model, so that the process corner module and the global fluctuation module run outside the circuit model.
In the foregoing solution, the arranging the process corner module, the global fluctuation module, the local fluctuation module, and the circuit model based on a preset arrangement rule further includes:
and setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model based on a preset arrangement rule, so that the local fluctuation module operates in the circuit model.
In the above scheme, after the statistical model of the semiconductor device is formed, the method includes:
receiving a first control parameter of the process corner module, a second control parameter of the global fluctuation module, a third control parameter of the local fluctuation module and a statistical parameter; the first control parameter, the second control parameter, the third control parameter, and the statistical parameter are determined according to a characteristic of the semiconductor device.
In the foregoing solution, the statistical parameters include: the first Gaussian distribution parameter of the global fluctuation module and the second Gaussian distribution parameter of the local fluctuation module.
The present invention also provides a modeling apparatus for a statistical model of a semiconductor device, the apparatus comprising:
the dividing unit is used for dividing the sub-modules of the semiconductor device statistical model into a process angle module, a global fluctuation module and a local fluctuation module;
a building unit for building a circuit model of the semiconductor device;
and the arrangement unit is used for arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form the semiconductor device statistical model.
In the foregoing solution, the arrangement unit is specifically configured to:
and based on a preset arrangement rule, the process corner module and the global fluctuation module are arranged outside the initial running identifier or the ending running identifier of the circuit model, so that the process corner module and the global fluctuation module run outside the circuit model.
In the foregoing solution, the arrangement unit is further specifically configured to:
and setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model based on a preset arrangement rule, so that the local fluctuation module operates in the circuit model.
In the above scheme, the apparatus further comprises: the receiving unit is used for receiving a first control parameter of the process corner module, a second control parameter of the global fluctuation module, a third control parameter of the local fluctuation module and a statistical parameter; the first control parameter, the second control parameter, the third control parameter, and the statistical parameter are determined according to a characteristic of the semiconductor device.
In the foregoing solution, the statistical parameters include: the first Gaussian distribution parameter of the global fluctuation module and the second Gaussian distribution parameter of the local fluctuation module.
The invention provides a modeling method and a device of a semiconductor device statistical model, wherein the method comprises the following steps: sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module; establishing a circuit model of the semiconductor device; arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form a semiconductor device statistical model; therefore, when the statistical model of the semiconductor device is established, the process corner module is combined, and the influence of the process corner on the performance of the semiconductor device is considered, so that the analysis precision is higher when the established process model of the semiconductor device analyzes the influence of the process fluctuation on the performance of the device, and the precision of the semiconductor device can be further improved.
Drawings
Fig. 1 is a schematic flow chart of a modeling method of a statistical model of a semiconductor device according to an embodiment of the present invention;
fig. 2 is a schematic overall structural diagram of a modeling apparatus for a statistical model of a semiconductor device according to a second embodiment of the present invention.
Detailed Description
The method aims to solve the technical problem that when a device statistical model is used for analyzing the influence degree of process fluctuation on the device performance in the prior art, the analysis precision is low when the influence of the statistical model on the device performance is analyzed due to certain difficulty in combination with a process angle model; the invention provides a modeling method and a device of a semiconductor device statistical model, wherein the method comprises the following steps: sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module; establishing a circuit model of the semiconductor device; and arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form the semiconductor device statistical model.
The technical solution of the present invention is further described in detail by the accompanying drawings and the specific embodiments.
Example one
The embodiment provides a modeling method of a semiconductor device statistical model, as shown in fig. 1, the method includes:
s101, sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module;
in the embodiment, in order to improve the analysis accuracy of the semiconductor device statistical model when the semiconductor device statistical model analyzes the influence of process fluctuation on the device performance, the sub-module of the semiconductor device statistical model is divided into a process angle module, a global fluctuation module and a local fluctuation module.
Specifically, the global fluctuation refers to fluctuation among lots of lot, wafers of the same lot, or dies of the same wafer, and is generally caused by fluctuation of macro processes such as heat, gas, and the like, and the fluctuation has the same influence on devices in the same chip. Local fluctuation refers to the difference between devices with the same size structure and adjacent positions in the same chip, which is generally determined by the randomness of the micro-process, and the fluctuation has different influences on the devices in the same chip.
However, due to the process deviation, even if the chip on the same silicon wafer has different device attributes at different positions, the process deviation needs to be considered when designing the chip, and the chip can normally work under various process angles in the design stage, so that the final chip can be reliable. It is therefore also desirable to analyze the impact of process corner on device performance.
S102, establishing a circuit model of the semiconductor device;
sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module; a circuit model of the semiconductor device, which is generally referred to as a sub-circuit model, is also created.
Here, the same actual circuit should be simulated with different sub-circuit models, due to different requirements on the accuracy of the circuit model and different operating conditions of the actual circuit.
S103, arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form a semiconductor device statistical model;
after the circuit model is established, the process corner module, the global fluctuation module, the local fluctuation module and the circuit model are sequentially arranged and guided in based on a preset arrangement rule to form the semiconductor device statistical model.
Specifically, in order to improve the analysis accuracy, the process corner module and the global fluctuation module are arranged outside the initial operation identifier or the end operation identifier of the circuit model based on a preset arrangement rule, so that the process corner module and the global fluctuation module operate outside the circuit model. And setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model, so that the local fluctuation module operates in the circuit model. The initial identification may be a name of the circuit model.
After the statistical model of the semiconductor device is formed, receiving a first control parameter of the process corner module, a second control parameter of the global fluctuation module, a third control parameter of the local fluctuation module and a statistical parameter; the first control parameter, the second control parameter, the third control parameter, and the statistical parameter are determined according to a characteristic of the semiconductor device. The semiconductor device may include: all semiconductor devices such as resistors and capacitors are not listed here.
Wherein the statistical parameters include: the first Gaussian distribution parameter of the global fluctuation module and the second Gaussian distribution parameter of the local fluctuation module. It should be noted that the statistical model is applicable to all semiconductor devices, the first gaussian distribution parameters in different statistical models of semiconductor devices may be set according to actual requirements, and the second gaussian distribution parameters in different statistical models of semiconductor devices may be set according to actual requirements.
Example two
Corresponding to the first embodiment, this embodiment provides a modeling apparatus for a statistical model of a semiconductor device, as shown in fig. 2, the apparatus including: a dividing unit 21, a building unit 22, an arranging unit 23 and a receiving unit 24; wherein,
in order to improve the analysis accuracy of a semiconductor device statistical model when the statistical model analyzes the influence of process fluctuation on the device performance, the dividing unit 21 is configured to divide sub-modules of the semiconductor device statistical model into a process corner module, a global fluctuation module and a local fluctuation module; specifically, the global fluctuation refers to fluctuation among lots of lot, wafers of the same lot, or dies of the same wafer, and is generally caused by fluctuation of macro processes such as heat, gas, and the like, and the fluctuation has the same influence on devices in the same chip. Local fluctuation refers to the difference between devices with the same size structure and adjacent positions in the same chip, which is generally determined by the randomness of the micro-process, and the fluctuation has different influences on the devices in the same chip.
However, due to the process deviation, even if the chip on the same silicon wafer has different device attributes at different positions, the process deviation needs to be considered when designing the chip, and the chip can normally work under various process angles in the design stage, so that the final chip can be reliable. It is therefore also desirable to analyze the impact of process corner on device performance.
The dividing unit 21 divides the submodules of the semiconductor device statistical model into a process angle module, a global fluctuation module and a local fluctuation module; the establishing unit 22 is used for establishing a circuit model of the semiconductor device; here, the same actual circuit should be simulated with different sub-circuit models, due to different requirements on the accuracy of the circuit model and different operating conditions of the actual circuit. After the circuit model is built by the building unit 22, the arranging unit 23 is configured to arrange the process corner module, the global fluctuation module, the local fluctuation module, and the circuit model based on a preset arrangement rule to form the statistical model of the semiconductor device.
Specifically, the arranging unit 23 sets the process corner module and the global fluctuation module outside the operation initial identifier or the operation end identifier of the circuit model based on a preset arrangement rule, and the process corner module and the global fluctuation module operate outside the circuit model. And setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model, wherein the local fluctuation module operates in the circuit model. The initial identification may be a name of the circuit model.
After the statistical model of the semiconductor device is formed, the receiving unit 24 receives the first control parameter of the process corner module, the second control parameter of the global fluctuation module, the third control parameter of the local fluctuation module, and the statistical parameter; the first control parameter, the second control parameter, the third control parameter, and the statistical parameter are determined according to a characteristic of the semiconductor device. The semiconductor device may include: all semiconductor devices such as resistors and capacitors are not listed here.
Wherein the statistical parameters include: the first Gaussian distribution parameter of the global fluctuation module and the second Gaussian distribution parameter of the local fluctuation module. It should be noted that the statistical model is applicable to all semiconductor devices, the first gaussian distribution parameters in different statistical models of semiconductor devices may be set according to actual requirements, and the second gaussian distribution parameters in different statistical models of semiconductor devices may be set according to actual requirements.
EXAMPLE III
In practical application, when the modeling method provided in the first embodiment and the modeling apparatus provided in the second embodiment are used to model a resistance statistical model, the following concrete implementation is performed:
in the embodiment, in order to improve the analysis accuracy of the resistance statistical model when the resistance statistical model analyzes the influence of the process fluctuation on the device performance, the sub-module of the resistance statistical model is divided into a process corner module global fluctuation module and a local fluctuation module.
Specifically, the global fluctuation refers to fluctuation among lots of lot, wafers of the same lot, or dies of the same wafer, and is generally caused by fluctuation of macro processes such as heat, gas, and the like, and the fluctuation has the same influence on devices in the same chip. Local fluctuation refers to the difference between devices with the same size structure and adjacent positions in the same chip, which is generally determined by the randomness of the micro-process, and the fluctuation has different influences on the devices in the same chip.
However, due to the process deviation, even if the chip on the same silicon wafer has different device attributes at different positions, the process deviation needs to be considered when designing the chip, and the chip can normally work under various process angles in the design stage, so that the final chip can be reliable. It is therefore also desirable to analyze the impact of process corner on device performance.
Sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module; a circuit model of the semiconductor device, which is generally referred to as a sub-circuit model, is also created. Here, the same actual circuit should be simulated with different sub-circuit models, due to different requirements on the accuracy of the circuit model and different operating conditions of the actual circuit. In this embodiment, the circuit model is subbckt _ res _ mis 10w 1u l 1 ullobalmod 1 mismod 1; wherein the globalmod is a global fluctuation module, and the mismod is a local fluctuation module.
After the circuit model is built, the process corner module, the global fluctuation module, the local fluctuation module and the circuit model are sequentially arranged and led in based on a preset arrangement rule, the process corner module and the global fluctuation module are arranged outside an initial operation identifier or an end operation identifier of the circuit model based on the preset arrangement rule, and the process corner module and the global fluctuation module operate outside the circuit model. And setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model, wherein the local fluctuation module operates in the circuit model to form the resistance statistical model. The running initial identification may be the name of the circuit model subbckt _ res _ mis 10 w.
After the semiconductor device statistical model is formed, receiving a first control parameter paramdrsh _ corner of the process corner module, a second control parameter drsh _ global of the global fluctuation module, a third control parameter paramdrsh _ mis of the local fluctuation module and a statistical parameter; the first control parameter param drsh _ corner, the second control parameter drsh _ global, the third control parameter param drsh _ mis, and the statistical parameter are determined according to the characteristic of the resistance.
Wherein the statistical parameters include: the first Gaussian distribution parameter param sigma _ a of the global fluctuation module and the second Gaussian distribution parameter param sigma _ mis _ a of the local fluctuation module. It should be noted that the statistical model is applicable to all semiconductor devices.
Then, when the process corner module and the global fluctuation module are disposed outside the initial operating identifier of the circuit model, the resistance statistical model in this embodiment may specifically be:
lib res_mc
process corner portion
.param drsh_corner＝0
Global fluctuating fraction
.param sigma_a＝agauss(0,1,3)
+drsh_global＝’10*sigma_a’
Gaussian distribution required for local fluctuation
.param sigma_mis_a＝agauss(0,0.5,3)
.subckt_res_mis 1 0w＝1u l＝1u globalmod＝1 mismod＝1
Local undulating portion
.param drsh_mis＝’10*sigma_mis_a’
Total of
.param drsh＝’drsh_corner+drsh_global*globalmod+drsh_mis*mismod’
R1 1 0resmod w＝w l＝l
.model resmod r
+rsh＝’20+drsh’
.ends res_mis**
.endl res_mis_mc
Then, when the process corner module and the global fluctuation module are disposed outside the operation end identifier of the circuit model, the resistance statistical model in this embodiment may further be:
gaussian distribution required for local fluctuation
.param sigma_mis_a＝agauss(0,0.5,3)
.subckt_res_mis 1 0w＝1u l＝1u globalmod＝1mismod＝1
Local undulating portion
.param drsh_mis＝’10*sigma_mis_a’
Total of
.param drsh＝’drsh_corner+drsh_global*globalmod+drsh_mis*mismod’
R1 1 0resmod w＝w l＝l
.model resmod r
+rsh＝’20+drsh’
.ends res_mis**
lib res_mc
Process corner portion
.param drsh_corner＝0
Global fluctuating fraction
.param sigma_a＝agauss(0,1,3)
+drsh_global＝’10*sigma_a’
.endl res_mis_mc
As can be seen from the two resistance models, the process corner module param drsh _ kernel and the global fluctuation module drsh _ global are arranged outside the initial running identifier subbckt _ res _ mis 10w or the ending running identifier endsres _ mis of the circuit model, that is, the process corner module param drsh _ kernel and the global fluctuation module drsh _ global run outside the circuit model; and the local fluctuation module is arranged between the circuit model operation initial identification subbckt _ res _ mis 10w and the operation ending identification endres _ mis, namely the local fluctuation module operates inside the circuit model.
It should be noted that in the resistance statistical model, sigma _ a is param sigma _ a in the global fluctuation part; in the local fluctuation part, sigma _ a is param sigma _ mis _ a;
the last variable calculated by the statistical model is param drsh, and the value of the variable can be calculated by the formula param drsh ═ drsh _ kernel + drsh _ global × (globalmo + drsh _ mis) × mismod'.
The true variable rsh calculated by the model is calculated by the formula + rsh ═ 20+ drsh', and the reference value is 20. And the change value of the real variable is the influence value of the process fluctuation on the device.
Here, the resistance statistical model in this embodiment is a basic statistical model, and resistance statistical models at other process corners can be obtained through the statistical model, for example, under FF of fast operation of the device, it is only necessary to set globalmod to 0, mismod to 1, and otherwise change drsh _ corner.
If the circuit to be simulated is only sensitive to global fluctuations, globalmod can also be set to 1 and mismod to 0.
The modeling method and the device of the semiconductor device statistical model provided by the embodiment of the invention have the following beneficial effects that:
the embodiment of the invention provides a modeling method and a device of a semiconductor device statistical model, wherein the method comprises the following steps: sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module; establishing a circuit model of the semiconductor device; arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form a semiconductor device statistical model; therefore, when the statistical model of the semiconductor device is established, the process corner module is combined, and the influence of the process corner on the performance of the semiconductor device is considered, so that the analysis precision is higher when the established process model of the semiconductor device analyzes the influence of the process fluctuation on the performance of the device, and the precision of the semiconductor device can be further improved. In addition, the statistical model established by the method only needs to change the values of param drsh corner parameter, globalmo and mismod under different scenes (such as different mean values TT, FF or process angles), so that the management and maintenance of the statistical model are facilitated.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.
Claims (10)
1. A method of modeling a statistical model of a semiconductor device, the method comprising:
sub-modules of the semiconductor device statistical model are divided into a process angle module, a global fluctuation module and a local fluctuation module;
establishing a circuit model of the semiconductor device;
and arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form the semiconductor device statistical model.
2. The method of claim 1, wherein the arranging the process corner module, the global fluctuation module, the local fluctuation module, and the circuit model based on a preset arrangement rule comprises:
and based on a preset arrangement rule, the process corner module and the global fluctuation module are arranged outside the initial running identifier or the ending running identifier of the circuit model, so that the process corner module and the global fluctuation module run outside the circuit model.
3. The method of claim 1, wherein the arranging the process corner module, the global fluctuation module, the local fluctuation module, and the circuit model based on a preset arrangement rule further comprises:
and setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model based on a preset arrangement rule, so that the local fluctuation module operates in the circuit model.
4. The method of claim 1, wherein forming the statistical model of the semiconductor device comprises:
receiving a first control parameter of the process corner module, a second control parameter of the global fluctuation module, a third control parameter of the local fluctuation module and a statistical parameter; the first control parameter, the second control parameter, the third control parameter, and the statistical parameter are determined according to a characteristic of the semiconductor device.
5. The method of claim 4, wherein the statistical parameters comprise: the first Gaussian distribution parameter of the global fluctuation module and the second Gaussian distribution parameter of the local fluctuation module.
6. An apparatus for modeling a statistical model of a semiconductor device, the apparatus comprising:
the dividing unit is used for dividing the sub-modules of the semiconductor device statistical model into a process angle module, a global fluctuation module and a local fluctuation module;
a building unit for building a circuit model of the semiconductor device;
and the arrangement unit is used for arranging the process corner module, the global fluctuation module, the local fluctuation module and the circuit model based on a preset arrangement rule to form the semiconductor device statistical model.
7. The apparatus of claim 6, wherein the ranking unit is specifically configured to:
and based on a preset arrangement rule, the process corner module and the global fluctuation module are arranged outside the initial running identifier or the ending running identifier of the circuit model, so that the process corner module and the global fluctuation module run outside the circuit model.
8. The apparatus of claim 6, wherein the ranking unit is further specifically configured to:
and setting the local fluctuation module between the operation initial identifier and the operation ending identifier of the circuit model based on a preset arrangement rule, so that the local fluctuation module operates in the circuit model.
9. The apparatus of claim 6, wherein the apparatus further comprises: the receiving unit is used for receiving a first control parameter of the process corner module, a second control parameter of the global fluctuation module, a third control parameter of the local fluctuation module and a statistical parameter; the first control parameter, the second control parameter, the third control parameter, and the statistical parameter are determined according to a characteristic of the semiconductor device.
10. The apparatus of claim 9, wherein the statistical parameters comprise: the first Gaussian distribution parameter of the global fluctuation module and the second Gaussian distribution parameter of the local fluctuation module.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109684733A (en) * | 2018-12-26 | 2019-04-26 | 南京九芯电子科技有限公司 | A kind of generation and analysis method of TFT device angle model |
| CN110895643A (en) * | 2019-09-02 | 2020-03-20 | 芯创智（北京）微电子有限公司 | Memory reliability simulation verification method and device and storage medium |
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