This study presents a biomimetic bimodal haptic perception system (BITS) inspired by the campaniform sensilla on insect antennae, utilizing the triboelectric effect to detect material type, softness, and quantified modulus. The system features a hemispherical electrode and polymer triboelectric layer, enabling accurate identification of material types (99.4% accuracy), softness (100% accuracy), and modulus quantification. The triboelectric output fingerprints vary with material deformability, and the system uses machine learning to recognize material types based on triboelectric series positions. The system also incorporates a pressure sensor to measure contact force and employs the Hertz model to calculate Young's modulus by analyzing contact area and pressure. TheBITS array can detect contact height, pressure, and electron affinity to identify material type and softness. The system demonstrates high accuracy in identifying softness and quantifying modulus, with consistent results across various materials and environmental conditions. The system is capable of real-time haptic perception and has potential applications in wearable electronics, exploration robots, intelligent prosthetics, and augmented reality. The study highlights the potential of triboelectric effect-based sensors for multimodal tactile perception, offering a promising solution for human-machine integration and medical rehabilitation.This study presents a biomimetic bimodal haptic perception system (BITS) inspired by the campaniform sensilla on insect antennae, utilizing the triboelectric effect to detect material type, softness, and quantified modulus. The system features a hemispherical electrode and polymer triboelectric layer, enabling accurate identification of material types (99.4% accuracy), softness (100% accuracy), and modulus quantification. The triboelectric output fingerprints vary with material deformability, and the system uses machine learning to recognize material types based on triboelectric series positions. The system also incorporates a pressure sensor to measure contact force and employs the Hertz model to calculate Young's modulus by analyzing contact area and pressure. TheBITS array can detect contact height, pressure, and electron affinity to identify material type and softness. The system demonstrates high accuracy in identifying softness and quantifying modulus, with consistent results across various materials and environmental conditions. The system is capable of real-time haptic perception and has potential applications in wearable electronics, exploration robots, intelligent prosthetics, and augmented reality. The study highlights the potential of triboelectric effect-based sensors for multimodal tactile perception, offering a promising solution for human-machine integration and medical rehabilitation.