In this paper, we propose an efficient design methodology for energy-efficient off-chip interconnect. This approach leverages an artificial neural network (ANN) as a surrogate model that significantly improves design efficiency in the frequency-domain. This model utilizes design specifications as the constraint functions to guarantee the satisfaction of design requirements. Additionally, a specified objective function to select low-loss and low-noise structure is employed to determine the optimal case from a large design space. The proposed design flow can find the optimum design that gives maximum eye height (EH) with the largest allowable transmitter supply voltage ($V_{TX}$) reduction for minimum power consumption. The proposed approach is applied to the microstrip line and stripline structures with single-ended and differential signals for general applicability. For the microstrip line, the proposed methodology performs at a performance speed with 42.7 and 0.5 s per structure for the data generation and optimization process, respectively. In addition, the optimal microstrip line design achieves a 25%$V_{TX}$ reduction. In stripline structures, it takes 31.9 s for the data generation and 0.6 s for the optimization process per structure when the power efficiency reaches a maximum 30.7% peak to peak $V_{TX}$ decrease.

This article presents a length-dependent deep neural network (LD-DNN) based channel modeling methodology to predict the frequency response of high-speed channels. The proposed method significantly enhances the model accuracy and design efficiency while considering the channel length dependence that was neglected in previous modeling approaches. We define the concept of the electrical length to model the length and frequency dependence, then further leverage the activation function to capture the multiple reflection effects to improve accuracy. Additionally, we model the insertion loss resonance induced by crosstalk that can seriously deteriorate signal integrity. As a result, by adopting the proposed model which can predict the S-parameters as a function of length, the need for performing additionally 3D electromagnetic simulations when adjusting the channel length can be eliminated. Various high-speed channel cases are tested to validate the accuracy of the proposed method. The modeling accuracy is less than 4% for different high-speed channel structures with run times of less than 1.4 second per design.

In this work, we propose a signal integrity and power efficiency optimization framework for high-speed I/O equalizer systems using deep reinforcement learning (DRL) based on a deep double Q-network. Although equalizer techniques have long existed, few studies have systematically optimized the feed-forward equalizer (FFE), decision feedback equalizer (DFE), and continuous-time linear equalizer (CTLE) together, including the high-speed channel properties. This framework determines the parameters of the equalizer architecture to optimize performance metrics such as eye height, eye width, and power consumption. It offers three key advantages: (1) a framework with a scalable deep neural network (DNN) to efficiently model complex design challenges; (2) a future-state technique that mitigates design-space expansion with a minimum number of output neurons; and (3) simultaneous evaluation of power efficiency, unachievable with conventional methods. As a proof of concept, we present the optimization of equalizer architecture using the proposed approach across different high-speed channels, including a backplane and an interference-tolerant channel. Experimental results demonstrate that our approach improves signal and power performance significantly compared to those from conventional methods (e.g., minimum-mean squared error, least-mean squared) and converges much faster than genetic, particle swarm, and Bayesian optimization algorithms.