Abstract
Scanning spreading resistance microscopy (SSRM) with its high spatial resolution and high dynamic signal range is a powerful tool for two-dimensional characterization of semiconductor dopant areas. However, the application of the method is limited to devices in equilibrium condition, as the investigation of actively operated devices would imply potential differences within the device, whereas SSRM relies on a constant voltage difference between sample surface and probe tip. Furthermore, the standard preparation includes short circuiting of all device components, limiting applications to devices in equilibrium condition. In this work scanning dynamic voltage spreading resistance microscopy (SDVSRM), a new SSRM based two pass atomic force microscopy (AFM) technique is introduced, overcoming these limitations. Instead of short circuiting the samples during preparation, wire bond devices are used allowing for active control of the individual device components. SDVSRM consists of two passes. In the first pass the local sample surface voltage dependent on the dc biases applied to the components of the actively driven device is measured as in scanning voltage microscopy (SVM). The local spreading resistance is measured within the second pass, in which the afore obtained local surface voltage is used to dynamically adjust the terminal voltages of the device under test. This is done in a way that the local potential difference across the nano-electrical contact matches the software set SSRM measurement voltage, and at the same time, the internal voltage differences within the device under test are maintained. In this work the proof of the concept could be demonstrated by obtaining spreading resistance data of an actively driven photodiode test device. SDVSRM adds a higher level of flexibility in general to SSRM, as occurring differences in cross section surface voltage are taken into account. These differences are immanent for actively driven devices, but can also be present at standard, short circuited samples. Therefore, SDVSRM could improve the characterization under equilibrium conditions as well.
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