# Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction
###### Abstract  
The parameter extraction of device models is critically important for circuit simulation. The device models in the existing parameter extraction software are physics-based analytical models, or embedded Simulation program with integrated circuit emphasis (SPICE) functions. The programming implementation of physics-based analytical models is tedious and error prone, while it is time consuming to run the device model evaluation for the device model parameter extraction software by calling the SPICE. We propose a novel modeling technique based on a neural network (NN) for the optimal extraction of device model parameters in this paper, and further integrate the NN model into device model parameter extraction software. The technique does not require developers to understand the device model, which enables faster and less error-prone parameter extraction software developing. Furthermore, the NN model improves the extraction speed compared with the embedded SPICE, which expedites the process of parameter extraction. The technique has been verified on the BSIM-SOI model with a multilayer perceptron (MLP) neural network. The training error of the NN model is 4.14%, and the testing error is 5.38%. Experimental results show that the trained NN model obtains an extraction error of less than 6%, and its extraction speed is thousands of times faster than SPICE in device model parameter extraction. device model parameter extraction; device modeling; neural network; multilayer perceptron +
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extraction software by calling the SPICE, making the model parameter extraction less efficient. In recent years, NNs have been widely used due to their superior data processing capabilities. NNs provide smooth functions and infinite order derivatives that can approximate complex nonlinear functions [1,2]. Since the IV curves and other characteristics of devices are nonlinear, NNs have the potential to model them [3,4]. Currently, NNs have been applied in several modeling situations, such as small-signal modeling and microwave device modeling [5, 6, 7, 8]. As the complexity of the device model increases, the number of model parameters goes up. Manually specifying new model parameters for complex device physics equations is much harder than adding new variables to the NN model [9]. The artificial neural network (ANN) is applied to model the device, with a preprocessing of the current and voltage to train the ANN model accurately [10]. A device model based on the NN acts as an intermediate between early device measurements and a later occurring compact model [11]. A fitness function based on key electrical characteristics (the off current and the subthreshold slope) is proposed to compensate for the R2 functions acting solely as the fitness function. A novel physics-inspired neural network (Pi-NN) approach for compact modeling is proposed in [12], where fundamental device physics are incorporated in the NN model to improve the modeling accuracy. In this paper, we propose using an MLP neural network instead of a manually implemented physics-based analytical model or embedded SPICE to build a device model and integrate it into the parameter extraction software. The NN uses the IV and CV curves, and other characteristics for modeling, and learns the complex relationship between the input and output by training. The NN modeling technique does not require understanding the device model,  
Figure 1: The process of model parameter extraction. which enables faster and less error-prone development than the implementation of the physics-based analytical model. Moreover, the NN model improves the extraction speed over SPICE, which can expedite the process of parameter extraction. The NNs in these works [10; 11; 12] are only used for circuit simulation, and the inputs of the NNs only contain the voltages and the device geometries. We use the MLP neural network to build a device model for parameter extraction. The MLP neural network inputs include the voltages, device geometries, temperature, and model parameters.