CN114528805B - A CNN-based method for predicting electrical characteristics of FDSOI devices - Google Patents
A CNN-based method for predicting electrical characteristics of FDSOI devices Download PDFInfo
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Abstract
The present invention relates to the fields of microelectronics technology and artificial intelligence technology, and specifically relates to a method for predicting the electrical characteristics of FDSOI devices based on CNN networks. The present invention utilizes CNN networks to solve the problem of large computational complexity and long time consumption in traditional research processes for predicting the electrical characteristics of FDSOI devices, and has high accuracy, improving the efficiency of prediction.
Description
Technical Field
The invention relates to the technical field of microelectronics and artificial intelligence, in particular to a method for predicting electrical characteristics of an FDSOI (fully depleted silicon on insulator) device based on a CNN (convolutional neural network).
Background
After the feature size of the integrated circuit process is reduced to 45nm, various non-ideal effects, such as short channel effect, drain induced barrier lowering effect and the like, can seriously affect the performance of the bulk silicon MOS transistor. SOI (silicon on insulator) technology has been proposed to solve this problem. Silicon-on-insulator, i.e., adding insulating material between silicon transistors, i.e., using a "silicon-insulator-silicon" device structure, greatly reduces parasitic capacitance between the two, thereby improving device performance. SOI technology is divided into FDSOI technology and PDSOI (partially depleted silicon on insulator) technology. Compared with the PDSOI technology, the FDSOI technology has thinner silicon film thickness, and the depletion layer below the device is filled with the whole silicon layer when the device works, so that the short channel effect can be better controlled, the source-drain junction depth is limited, and the subthreshold characteristic is improved. Therefore, the FDSOI technology becomes a good choice of small-size process, and has great significance for the research of FDSOI devices.
The traditional research of the FDSOI technology is mostly based on simulation software, has large calculation amount and long time consumption, and the CNN network is used as one of representative algorithms of deep learning, can automatically extract features according to data, has characteristic learning ability and is attracting more attention.
In recent years, CNN networks have achieved tremendous success in research fields such as speech recognition, image segmentation, natural language processing, and the like, and their rapid development provides other ideas for predicting the electrical characteristics of devices.
Disclosure of Invention
The invention aims to provide a method for predicting the electrical characteristics of an FDSOI device based on a CNN network, which solves the problems of large calculation amount and long time consumption in the traditional research process of predicting the electrical characteristics of the FDSOI device.
In order to achieve the above purpose, the present invention is realized by the following technical scheme.
A FDSOI device electrical characteristic prediction method based on a CNN network comprises the following steps:
step1, obtaining physical parameters and corresponding electrical characteristics of a plurality of groups of FDSOI devices as a sample set;
The physical parameters of the FDSOI device comprise gate length, buried oxide layer thickness, body region doping concentration and gate metal work function, wherein the electrical characteristics of the FDSOI device comprise threshold voltage, transconductance, subthreshold swing and current switching ratio;
step 2, randomly dividing the data in the sample set into a training set, a cross validation set and a test set according to a proportion;
Step 3, constructing a CNN network prediction model;
Step 4, inputting training set data into the CNN network prediction model, and carrying out iterative updating on parameters of the CNN network prediction model by using a regression loss function to obtain a trained CNN network prediction model; inputting the test set data into the CNN network prediction model after training, and evaluating the prediction accuracy and generalization capability of the CNN network prediction model;
and 5, inputting physical parameters of the FDSOI device to be predicted into a trained CNN network prediction model, and outputting an electrical characteristic prediction result of the FDSOI device by the trained CNN network prediction model.
Compared with the prior art, the method has the beneficial effects that the problems of large calculation amount and long time consumption in the traditional research process of predicting the electrical characteristics of the FDSOI device are solved by utilizing the CNN network, the method has higher accuracy and the prediction efficiency is improved.
Drawings
The invention will now be described in further detail with reference to the drawings and to specific examples.
FIG. 1 is a flow chart of a method for predicting electrical characteristics of an FDSOI device based on a CNN network;
FIG. 2 is a schematic diagram of a structure of an FDSOI device selected in an embodiment of the invention;
FIG. 3 is a graph of transfer characteristics of a selected FDSOI device in accordance with an embodiment of the present invention under the parameters of Table 1;
FIG. 4 is a schematic diagram of a CNN network prediction model structure according to the present invention;
FIG. 5 is a graph showing the comparison of predicted values and actual values of electrical characteristics of a test set according to an embodiment;
FIG. 5 (a) is a graph of predicted versus actual values of the test set switching current ratios in an embodiment;
FIG. 5 (b) is a graph of predicted versus actual values of test set subthreshold swing in an embodiment;
FIG. 5 (c) is a graph of predicted versus actual values of the threshold voltages of the test set in an embodiment;
FIG. 5 (d) is a graph comparing predicted and actual values of the test set transconductance in the example;
FIG. 6 is a classification chart of prediction accuracy of electrical characteristics of a test set according to an embodiment;
FIG. 6 (a) is a classification chart of test set switching current versus prediction accuracy in an embodiment;
FIG. 6 (b) is a classification statistical graph of test set subthreshold swing prediction accuracy in an embodiment;
FIG. 6 (c) is a classification statistical graph of test set threshold voltage prediction accuracy in an embodiment;
FIG. 6 (d) is a classification statistical graph of test set threshold voltage prediction accuracy in an embodiment.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only for illustrating the present invention and should not be construed as limiting the scope of the present invention.
Referring to fig. 1, a method for predicting electrical characteristics of an FDSOI device based on a CNN network includes the steps of:
Step 1, obtaining physical parameters and corresponding electrical characteristics of 900 groups of FDSOI devices as a sample set;
The physical parameters of the FDSOI device comprise gate length, buried oxide layer thickness, body region doping concentration and gate metal work function, wherein the electrical characteristics of the FDSOI device comprise threshold voltage, transconductance, subthreshold swing and current switching ratio;
namely, each group of data comprises gate length, buried oxide layer thickness, body region doping concentration, gate metal work function, threshold voltage, transconductance, subthreshold swing and current switching ratio;
Specifically, the threshold voltage is a gate voltage value of the FDSOI device when the drain current is 1× -5 A/μm;
The transconductance is the maximum transconductance value in the transfer characteristic curve of the FDSOI device, and the transconductance calculation formula is shown in formula (1):
In the formula (1), g m is transconductance, I d is leakage current, and V g is gate voltage;
The subthreshold swing calculation formula is shown as (2):
In the formula (2), SS is a subthreshold swing, I d is a leakage current when the gate voltage is a threshold voltage in a transfer characteristic curve of the FDSOI device, and V g is a gate voltage when the gate voltage is the threshold voltage in the transfer characteristic curve of the FDSOI device;
The current switching ratio is the ratio of the leakage current of the FDSOI device when the gate voltage is 1V to the leakage current of the FDSOI device when the gate voltage is 0V.
Referring to fig. 2, a schematic structural diagram of an FDSOI device used in the present embodiment is shown;
referring to fig. 3, the FDSOI device used in the present embodiment has good characteristics in the linear region (V D =0.1v) and the saturation region (V D =1.0v) under the parameters of table 1, and can be used for prediction of electrical characteristics.
TABLE 1
| Grid length (L) | 28nm | Back grid concentration (Nbp) | 2e18cm-3 |
| Buried oxide layer thickness (Tbox) | 20nm | Substrate doping concentration (Nsub) | 1e14cm-3 |
| Gate dielectric thickness (Tox) | 1.2nm | Body doping concentration (Nbd) | 1e18cm-3 |
| Back gate thickness (Tbp) | 25nm | Source region doping concentration (Ns) | 4.4e20cm-3 |
| Metal Work Function (WF) | 4.52eV | Drain doping concentration (Nd) | 1e20cm-3 |
The physical parameters in 900 groups of data of the sample set are self-designed, the corresponding electrical characteristics of each group of data are obtained by TCAD software simulation, and the values of the physical parameters in 900 groups of data of the sample set are as follows:
the gate lengths are respectively 16nm, 20nm, 24nm, 28nm and 32nm, the thickness of the buried oxide layer is respectively 10nm, 20nm, 30nm, 40nm, 50nm and 60nm, and the doping concentration of the body region is respectively 1×1018cm-3、2×1018cm-3、3×1018cm-3、4×1018cm-3、5×1018cm-3,, and the work functions of the gate metals are respectively 4.44eV, 4.48eV, 4.52eV, 4.56eV, 4.60eV and 4.64eV.
Step 2, randomly dividing 900 groups of data in the sample set into a training set, a cross validation set and a test set according to a proportion;
Specifically, 60% of the sample set is a training set, 20% is a cross validation set, and the remaining 20% is a test set, namely 540 sets of data are used as the training set, 180 sets of data are used as the cross validation set, and 180 sets of data are used as the test set;
The training set is used for carrying out parameter training on the CNN network prediction model, the cross verification set is used for verifying whether the CNN network prediction model is subjected to fitting, and the test set is used for evaluating the accuracy and generalization capability of the CNN network prediction model;
Step 3, constructing a CNN network prediction model;
Specifically, the CNN network prediction model is input FDSOI device physical parameters, predicts the electrical characteristics of the FDSOI device and outputs the FDSOI device physical parameters;
referring to fig. 4, the CNN network prediction model includes an input expansion module, a transposed convolution module, a convolution module, and a full connection module connected in sequence;
the input expansion module comprises 3 layers, wherein each layer comprises a Linear layer (full connection layer), a BN layer (batch normalization layer) and a ReLU activation function which are sequentially connected;
the transpose convolution module comprises a ConvT layer (transpose convolution layer), a BN layer, a ConvT layer and a BN layer which are sequentially connected;
the convolution module comprises 3 layers, wherein each layer comprises a layer of convolution layer (Conv layer), a layer of BN layer and a layer of ReLU activation function which are sequentially connected;
the full-connection module comprises 3 layers, wherein each layer comprises a Linear layer, a BN layer and a ReLU activation function which are sequentially connected;
The input expansion module is used for expanding input data to a higher dimension, the transpose convolution module is used for improving the size of the characteristic dimension, the convolution module is used for extracting data characteristics, and the full-connection module is used for mapping the distributed characteristics learned by the network model to a sample marking space;
The BN layer is used for accelerating convergence speed of a CNN network prediction model and preventing overfitting, and a layer of ReLU activation function is added on the input expansion module, the convolution module and the full connection module and used for finishing nonlinear change of data and avoiding gradient explosion and gradient disappearance.
Step 4, inputting training set data into the CNN network prediction model, and carrying out iterative updating on parameters of the CNN network prediction model by using a regression loss function to obtain a trained CNN network prediction model; inputting the test set data into the CNN network prediction model after training, and evaluating the prediction accuracy and generalization capability of the CNN network prediction model;
Specifically, the gate length, the thickness of the buried oxide layer, the doping concentration of the body region and the work function of the gate metal of each group of data are taken as input data, and the threshold voltage, the transconductance, the subthreshold swing and the current switching ratio of each group of data are taken as output labels;
specifically, the regression loss function is a mean square error MSE function, that is, the sum of squares of the difference between the predicted value and the target value is calculated, and the calculation formula is shown in formula (3):
in the formula (3), n is the number of samples, y i represents the true value of the ith sample, Is the predicted value of the i-th sample.
The test set data is input into a CNN network prediction model which is completed by training, the CNN network prediction model which is completed by training outputs predicted electrical characteristics, the predicted electrical characteristics are compared with actual electrical characteristics, the comparison result is shown in figure 5, and the prediction accuracy is shown in figure 6.
Referring to fig. 5 (a), 5 (b), 5 (c) and 5 (d), the fitting effect of the predicted electrical characteristics and the actual electrical characteristics is better, and the mean square error of the test set is 0.001, wherein the fitting effect of the transconductance and the subthreshold swing is better than the fitting effect of the threshold voltage and the current switching ratio.
Referring to fig. 6 (a), 6 (b), 6 (c) and 6 (d), the prediction accuracy of the four electrical characteristics is high, the prediction accuracy is basically over 90%, in 180 groups of test set data, the prediction accuracy of the four electrical characteristics of data groups with over 90% reaches 95%, and the subthreshold swing prediction accuracy of all the test set data is over 95%.
And 5, inputting physical parameters of the FDSOI device to be predicted into a trained CNN network prediction model, and outputting an electrical characteristic prediction result of the FDSOI device by the trained CNN network prediction model.
While the invention has been described in detail in this specification with reference to the general description and the specific embodiments thereof, it will be apparent to one skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (4)
1. The FDSOI device electrical characteristic prediction method based on the CNN network is characterized by comprising the following steps of:
step1, obtaining physical parameters and corresponding electrical characteristics of a plurality of groups of FDSOI devices as a sample set;
The physical parameters of the FDSOI device comprise gate length, buried oxide layer thickness, body region doping concentration and gate metal work function, wherein the electrical characteristics of the FDSOI device comprise threshold voltage, transconductance, subthreshold swing and current switching ratio;
step 2, randomly dividing the data in the sample set into a training set, a cross validation set and a test set according to a proportion;
Step 3, constructing a CNN network prediction model, wherein the CNN network prediction model is used for inputting physical parameters of the FDSOI device, predicting the electrical characteristics of the FDSOI device and outputting the electrical characteristics;
the CNN network prediction model comprises an input expansion module, a transposition convolution module, a convolution module and a full connection module which are connected in sequence;
The input expansion module comprises 3 layers, wherein each layer comprises a Linear layer, a BN layer and a ReLU activation function which are sequentially connected;
The transpose convolution module comprises a ConvT layer, a BN layer, a ConvT layer and a BN layer which are sequentially connected;
The convolution module comprises 3 layers, wherein each layer comprises a convolution layer, a BN layer and a ReLU activation function which are sequentially connected;
the full-connection module comprises 3 layers, wherein each layer comprises a Linear layer, a BN layer and a ReLU activation function which are sequentially connected;
Step 4, inputting training set data into the CNN network prediction model, and carrying out iterative updating on parameters of the CNN network prediction model by using a regression loss function to obtain a trained CNN network prediction model; inputting the test set data into the CNN network prediction model after training, and evaluating the prediction accuracy and generalization capability of the CNN network prediction model;
and 5, inputting physical parameters of the FDSOI device to be predicted into a trained CNN network prediction model, and outputting an electrical characteristic prediction result of the FDSOI device by the trained CNN network prediction model.
2. The method for predicting electrical characteristics of a FDSOI device based on a CNN network according to claim 1, wherein the threshold voltage, transconductance, subthreshold swing and current-to-switch ratio in step 1 are specifically the gate voltage value of the FDSOI device when the drain current is 1×10 -5 a/μm;
The transconductance is the maximum transconductance value in the transfer characteristic curve of the FDSOI device, and the transconductance calculation formula is shown in formula (1):
In the formula (1), g m is transconductance, I d is leakage current, and V g is gate voltage;
The subthreshold swing calculation formula is shown as (2):
In the formula (2), SS is a subthreshold swing, I d is a leakage current when the gate voltage is a threshold voltage in a transfer characteristic curve of the FDSOI device, and V g is a gate voltage when the gate voltage is the threshold voltage in the transfer characteristic curve of the FDSOI device;
The current switching ratio is the ratio of the leakage current of the FDSOI device when the gate voltage is 1V to the leakage current of the FDSOI device when the gate voltage is 0V.
3. The method for predicting electrical characteristics of a CNN-based FDSOI device according to claim 1, wherein the training set data in step 4 is input into a CNN network prediction model, specifically, the gate length, the buried oxide layer thickness, the body region doping concentration, and the gate metal work function of each set of data are used as input data, and the threshold voltage, the transconductance, the subthreshold swing, and the current switching ratio of each set of data are used as output labels.
4. The method for predicting electrical characteristics of a FDSOI device based on a CNN network according to claim 1, wherein the regression loss function in step 4 is specifically a mean square error MSE function, that is, a sum of squares of a difference between a predicted value and a target value is calculated, and a calculation formula thereof is shown in formula (3):
in the formula (3), n is the number of samples, y i represents the true value of the ith sample, Is the predicted value of the i-th sample.
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