The emergence of artificial intelligence has represented great potential in solving a wide
range of complex problems. However, traditional general-purpose chips based on von …

This year marks about half a century since the birth of the technique known as the partial
element equivalent circuit modeling approach. This method was initially conceived to model …

In this paper, for the first time, the deep gated recurrent unit (Deep GRU) is used as a new
macromodeling approach for nonlinear circuits. Similar to Long Short-Term Memory (LSTM) …

In the computer-aided design (CAD) area, the recurrent neural network (RNN) has shown
notable functionality in generating fast and high-performance models rather than the models …

This paper presents a novel 3D folded capacitive synaptic crossbar array designed for in-
memory computing architectures. In this architecture, the bitline is folded over the wordline to …

This article proposes a novel macromodeling method for high-frequency nonlinear circuits,
utilizing an attention-based deep recurrent neural network (ATDRNN). This method …

Time-domain macromodeling represents an effective technique for generating compact
models of the linear time-invariant portion of complex electromagnetic systems. Among the …

A new structure and macromodeling approach which is an advance over high-order
recurrent neural network named bidirectional high-order deep recurrent neural network …

Prompted by the continual advancements in artificial intelligence, the neuromorphic chip
based on a spiking neural network (SNN) has attracted considerable attention because of its …

The increasing integration density of resistive random access memory (RRAM) arrays leads
to a consequential rise in the significance of electro-thermal and parasitic effects in crossbar …