A Programmable Electronic Skin with Event‐Driven In‐Sensor Touch Differential and Decision‐Making

Abstract High‐precise, crosstalk‐free tactile perception offers an intuitive way for informative human‐machine interactions. However, the differentiation and labeling of touch position and strength require substantial computational space due to the cumbersome post‐processing of parallel data. Herein, a programmable and robust electronic skin (PR e‐skin) with event‐driven in‐sensor touch differential and perception, solving the inherent defects in the von Neumann framework is introduced. The PR e‐skin realizes feature simplification and reduction of data transmission by integrating the computing framework into sensing terminals. Furthermore, the event‐driven functional mode further greatly compresses untriggered redundant data. Benefiting from the minimal concise dataset, the PR e‐skin can directly differentiate touch position and pressure with swift response time (<0.3 ms). Robust carbon functional film ensures long‐term and stable implementation (>10 000 cycles) of the in‐sensor computing architectural feature. In a designable, continuous position detection with an extensive pressure range (210 kPa), which is an improvement of 5.5 times, the PR e‐skin can ultra‐sensitive extract trajectory sliding or rapping actions. Moreover, combined with customized neural network, a dual‐encryption recognition system is constructed based on slide action, reaching a high recognition accuracy of ≈98%, which reveals the great potential in intelligent interaction and security.