Application of neural networks in circuit analysis

New approaches and techniques are constantly introduced and adopted for circuit analysis and optimization problems. Recently, there is a growing interest in applying the potential of neural networks to many new fields apa

Device and circuit-level modeling using neural networks with faster training based on network sparsity

Recently, circuit analysis and optimization featuring neural-network models have been proposed, reducing the computational time during optimization while keeping the accuracy of physics-based models. We present a novel approach for fast training of such neural-network models based on the sparse matrix concept. The new training technique does not require any structure change in neural networks, but makes use of the inherent nature of neural networks that for each pattern some neuron activations are close to zero, and hence, have no effect on network outputs and weights update. Much of the computation effort is saved over standard training techniques, while achieving the same accuracy. FET device and VLSI interconnect modeling examples verified the proposed technique

A Neural Network Modeling Approach to Circuit Optimization and Statistical Design

The trend of using accurate models such as physics-based FET models, coupled with the demand for yield optimization results in a computationally challenging task. This paper presents a new approach to microwave circuit optimization and statistical design featuring neural network models at either device or circuit levels. At the device level, the neural network represents a physics-oriented FET model yet without the need to solve device physics equations repeatedly during optimization. At the circuit level, the neural network speeds up optimization by replacing repeated circuit simulations. This method is faster than direct optimization of original device and circuit models. Compared to existing polynomial or table look-up models used in analysis and optimization, the proposed approach has the capability to handle high-dimensional and highly nonlinear problems