Neuromorphic simulation is fundamental to the study of information processing mechanism of the human brain and can further inspire application development of event-driven spiking neural networks. However large-scale neuromorphic simulation requires massive parallelism on multi-chip processing and imposes great challenges on dealing with data transmission latency and congestion problems between chips, especially when the number of simulated neurons reaches to billions or even trillions level. In this paper, we propose an ultra-low-latency on-chip router together with a multicast routing algorithm that focuses on reducing global loads and balancing loads between links. Additionally, we build a large-scale neuromorphic simulation platform consisting of 64 FPGA chips and evaluate the proposed design on it. The experiment results suggest that this design benefits from the proposed multicast routing algorithm in global communication loads and simulation capacity. This work has 4.1% similar to 5.2% reduction of global loads comparing to previous works and can achieve a latency as low as 25ns and a maximum data throughput of 6.25Gbps/chip.