Non‐linear modeling of 0.18‐μM CMOS using neural network

Abstract

This paper describes an artificial-neural-network (ANN)based

Graphene Oxide Waveguide and Micro‐Ring Resonator Polarizers

“…[12,13] To implement polarization-selective devices, a number of schemes have been proposed and demonstrated, including those based on refractive prisms, [14,15] birefringent crystals, [16,17] fiber components, [18][19][20] and integrated waveguides. [21][22][23][24][25] Among them, integrated polarization-selective devices based on complementary metal-oxidesemiconductor (CMOS) compatible integrated platforms [26] offer advantages of compact footprint, high stability, mass producibility, and high scalability as functional building blocks for photonic integrated circuits (PICs). [2] Optical waveguides with metal cladding have been widely used to implement waveguide polarizers.…”

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

“…50 nm, a physics-based compact model is very difficult to develop [2][3][4]. In recent years artificial neural network (ANN) has been used for modelling of variety of transistors [5][6][7], as it avoids repeated solving of the complex transcendental equations of a traditional compact physical model, but still offers sufficient accuracy. An ANN model can be based on either a direct [8] or an indirect equivalent circuit approach [5][6][7].…”

“…In recent years artificial neural network (ANN) has been used for modelling of variety of transistors [5][6][7], as it avoids repeated solving of the complex transcendental equations of a traditional compact physical model, but still offers sufficient accuracy. An ANN model can be based on either a direct [8] or an indirect equivalent circuit approach [5][6][7]. As the indirect approach can model both non-linear dc and dynamic (ac) indirect approach our ANNmodel is based on the equivalent circuit [9] shown in Figure 1.…”

“…The complete equivalent circuit model of FinFET shown in Figure 1 can be divided into two parts: the intrinsic part of the device corresponding to the source of the non-linearities, and the extrinsic linear, bias independent parasitic. A similar indirect approach has been used for other devices [5][6][7], but this is the first application for FinFET modelling.…”

“…In our case E error p10 À4 is found for each intrinsic output circuit parameters across all bias. Once a network is 'well trained', the methodology given in [6,7] can be used to find the non-linear relationship between input x in and output x out .…”