# Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks
## 1 Introduction  
With the power consumption of data centers soaring rapidly, the sharp contradiction between computational performance and thermal limitations in the motherboard has become a major bottleneck confronting designers [1]. High power density and high-efficiency designs are extremely valuable for onboard DC-DC converters such as voltage regulator modules (VRMs) and point of load (POL) converters. Among various DC-DC converters, soft-switching solutions have greatly reduced switching losses under the irreversible trend of high frequency compared to conventional switching converters. Undesirably, they are difficult to integrate due to bulky inductive items. Furthermore, the inductor-based converter design suffers from second-order effects introduced by the nonlinear characteristics of magnetic materials. As one of most vital branches of DC-DC converters, switched-capacitor converters (SCCs) can be easily embedded on the motherboard, and are regarded as a promising solution [2, 3] for balancing performance and cooling. Currently, SCCs are widely used in chip power supply, energy buffering, LED driving, portable device fast charging, and data center power supply. SCCs are composed exclusively of capacitors and switches, and can efficiently generate a step-up or step-down output voltage of the same polarity with two or more phases. One of the most well-known switched capacitor circuits is the Dickson Charge Pump (DCP) [4]. Lacking magnetic elements [5], SCCs have an overwhelming advantage in reliability, integration level, control complexity, and power density compared with the conventional switching-mode power supply (SMPS) and soft-switching converter [6]. In order to cover existing and emerging application requirements, SCCs featuring diverse characteristics are in demand. Hence, continuous academic efforts have been directed towards SCC topology exploration, resulting in the rapid expansion of topologies of SCCs [7]. In general, there are three traditional approaches concerning topology derivation, namely, modular structure extension [8, 9], iterative combination [10], and systematic generation [11]. SCC topology design is a rather complicated issue due to the integrationand comprehensive effects of various parameters, such as power consumption, switch characteristics, and operation frequency [12; 13]. Conventionally, the selection of an SCC topology from the large variety of derived topologies is an extremely laborious process which requires iterative synthesis, simulation, evaluation, and optimization [14; 15]. This can significantly extend the development cycle due to repetitive efforts [11]. For the sake of economic cost, designers often fall into stereotypical solutions such as Dickson or Doubler without considering specific requirements, which can easily lead to non-optimal system design. Therefore, performance evaluation methods for new converter topologies are highly valued in switched-capacitor research. In [16], a new architecture for a charge pump and its design specifications for battery-powered applications were proposed, and a simplified RC model was built for mathematical analysis. However, this method can only be applied to simple topologies. Two models were proposed by [PERSON] et al. in [17] for a charge pump circuit design with a PMOS charge pump. The charge balance method and dynamic charge transfer waveforms were used to improve the output voltage and power conversion efficiency. In [18], the authors proposed a conduction loss model of an SCC based on the average-current model, with the conduction loss calculated using the total loop equivalent resistance. However, the current waveforms were obtained by approximation at the expense of accuracy. In general, complex topology, computational efficiency, and accuracy cannot all be achieved at the same time. With the advent of machine learning techniques, state-of-the-art AI approaches can open a door for creative means of SCC performance evaluation. In this paper, we present a new performance evaluation algorithm for SCCs.