Out-of-Training-Range Synthetic FinFET and Inverter Data Generation Using a Modified Generative Adversarial Network
Out-of-Training-Range Synthetic FinFET and Inverter Data Generation Using a Modified Generative Adversarial Network
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DOI：
10.1109/led.2022.3207784
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发表时间：
2022-11
影响因子：
4.9
通讯作者：
Vasu Eranki;Nathan Yee;H. Wong
中科院分区：
文献类型：
--
作者：
Vasu Eranki;Nathan Yee;H. Wong
In this letter, a novel variation of Generative Adversarial Network (GAN) is proposed and used to predict device and circuit characteristics based on design parameters. Unlike regular GAN which takes white noise as inputs, this modified GAN uses device or circuit parameters as inputs. Unlike regular Physics-informed GAN (PI-GAN) which incorporates differential equations in the training process, this modified GAN learns physics through the inputs and has one extra step of supervised learning. FinFET is used as a device example and Technology Computer-Aided-Design (TCAD) is used to generate its current-voltage ( ${I}_{D}{V}_{G}$ , ${I}_{D}{V}_{D}{)}$ and capacitance-voltage ( ${C}_{G}{V}_{G}$ ) curves as the training data by varying the gate length ( $\text{L}_{\text {G}}{)}$ , fin top width ( $\text{W}_{\text {TOP}}{)}$ , and gate metal workfunction (WF). A CMOS inverter with source contact defects is used as a circuit example and a SPICE simulator is used to generate its Voltage Transfer Characteristics (VTC) by varying the source contact resistances. We show that 1) the GAN model is able to generate both the device and circuit electrical characteristics based on the input parameters, 2) it can predict the characteristics of the device and circuit out of the training range (in a testing volume 3.7x to 4.6x larger than the training volume), and 3) it is further verified on experimentally measured data in the inverter case that it does not overfit and has learned the underlying physics.
In this letter, a novel variation of Generative Adversarial Network (GAN) is proposed and used to predict device and circuit characteristics based on design parameters. Unlike regular GAN which takes white noise as inputs, this modified GAN uses device or circuit parameters as inputs. Unlike regular Physics-informed GAN (PI-GAN) which incorporates differential equations in the training process, this modified GAN learns physics through the inputs and has one extra step of supervised learning. FinFET is used as a device example and Technology Computer-Aided-Design (TCAD) is used to generate its current-voltage ( ${I}_{D}{V}_{G}$ , ${I}_{D}{V}_{D}{)}$ and capacitance-voltage ( ${C}_{G}{V}_{G}$ ) curves as the training data by varying the gate length ( $\text{L}_{\text {G}}{)}$ , fin top width ( $\text{W}_{\text {TOP}}{)}$ , and gate metal workfunction (WF). A CMOS inverter with source contact defects is used as a circuit example and a SPICE simulator is used to generate its Voltage Transfer Characteristics (VTC) by varying the source contact resistances. We show that 1) the GAN model is able to generate both the device and circuit electrical characteristics based on the input parameters, 2) it can predict the characteristics of the device and circuit out of the training range (in a testing volume 3.7x to 4.6x larger than the training volume), and 3) it is further verified on experimentally measured data in the inverter case that it does not overfit and has learned the underlying physics.