Utilizing the superior etch stop quality of HfO_2 in the front end of line wafer scale integration of silicon nanowire biosensors

摘要

Silicon nanowire (SiNW) biosensors have received a special attention from the research community due to its ability to detect a range of species. The nano feature size of the SiNW has been exploited to fabricate small, low-cost, robust, portable, real-time read-out biosensors. These sensors are manufactured by two methods - top-down or bottom-up. Instead of the bottom-up method, the top-down approach is widely used due to its compatibility with complementary metal-oxide semiconductor (CMOS) process and scope of mass production. However, in the top-down method, the post fabrication microfluidic channel integration to access the SiNW test site remains complex and challenging. Since the nanosensor is expected to operate in a bio environment, it is essential to passivate the metal electrodes while pathways have to be made to access the test site. In this paper, we present a relatively easier method to access the SiNW test site without employing complex microfluidic channels while achieving leakage free passivation of metal electrodes and preserving the integrity of the nanosensor. This is accomplished in the last step of the manufacturing process by employing a lithography mask and reactive ion etching (RIE). HfO2 integrated crystalline silicon nanosensors are manufactured using novel top-down front end of line (FEOL) sidewall transfer lithography (STL) process. HfO2 acts as an etch stop layer while performing RIE in the last step to access the sensor test site. The 100 mm wafer scale results of 20 nm x 60 nm x 6 mu m (H x W x L) p-type nanosensors shows an average I-on/I-off = 10(5) with maximum turn-on voltage of - 4 V and uniform subthreshold slope of 70 mV/dec. In comparison with sensors encapsulated with SiO2, the HfO2 integrated nanosensors were found to improve the threshold voltage variation by 50%. Based on this work, the HfO2 integrated SiNW demonstrates good stability for biosensing application.