Edge Reinforcement of Ultrathin Crystal Silicon Enabling Stress Dispersion for Flexible Respiration Sensor

As a semiconductor material with exceptional optoelectronic properties, ultrathin crystal silicon (c-Si) is widely applied to wearable electronics. However, ultrathin c-Si is highly susceptible to concentrated stresses during processing due to microdefects at its edges or surfaces. This susceptibility leads to a vulnerable ultrathin c-Si substrate fracture rate, which impedes its development and widespread adoption. To tackle this challenge, we develop a method to enhance the strength of ultrathin c-Si substrates by depositing an edge reinforcement framework (ERF) layer composed of organic-based flexible materials via a microprinting technique. We investigate the fracturing mechanism by examining the breakage of c-Si under varying thicknesses and characterizing the side cross sections of the substrates. The critical factors influencing the reinforcement effect, namely, Young’s modulus of the flexible materials and their coupling interaction with the c-Si substrate surface, are identified to provide a key role for the framework on the reinforced ultrathin c-Si substrate. This approach successfully strengthens the mechanical properties of the c-Si substrates by 25%. A combination of Raman and theoretical simulations is conducted to elucidate the enhancement mechanism. High-performance respiration sensors with a 10-μm-thick c-Si substrate with a diagonal length exceeding 2 in. and a functional structure comprising ∼5-μm-long silicon nanowire arrays are fabricated to demonstrate its potential flexible application. These devices exhibit exceptional mechanical properties, including a bending radius of less than 5 mm and a rapid sensing response recovery rate with a time interval of less than 0.2 s, enabling them to be applied to wearable sensors. When integrated with a bidirectional long short-term memory neural network, the sensor achieves an impressive accuracy of 95% for distinguishing respiratory rates and rhythm, enabling its application in respiration sensing.