This paper discusses the so called Pressing Probe Needle (PPN) technique for characterizing out-of-plane mechanical stress sensitivity of arbitrary semiconductor devices, implemented on a standard parametric test system. By utilizing a motorized probe positioner and a force calibrated standard tungsten parametric probe needle, this fast and highspatial-resolution technique provides valuable insights into mechanical stress effects of process and device layout options. Such results are highly beneficial for high-precision analog circuit design and layout optimization.
Accurate modeling of the gate-drain capacitance C gd for HV LDMOS transistors is important but is challenging because of its strong bias dependence. We present an improved C gd model, based on the physics that the drift region under the poly-gate is fully depleted at high V dg , and validate our model against gate charge measurements for both n-and p-type 90V LDMOS transistors.
A new, cage-like structure is presented and is shown to be able to electrically identify a probe needle that has fallen slightly off its probe pad, even when the standard probe resistance structure (pads shorted together) reports “good” probe resistance. Using both structures together enables a more accurate evaluation of a probe system’s capabilities. Both test structures were used to compare three types of probe cards, reporting the smallest probe pad size that provides 100& probe yield.
The goal of the study is to monitor the device’s response during laser injection while being able to track pre- and post-attack conditions. We show the irradiation power affects the STT-MRAM behavior. Our electrical/optical setup enables to know the memory cell behavior to study real-time laser attack countermeasures and device reliability. We have highlighted the possibility to switch, to degrade or even to destruct the cell, depending on the laser power.
The variability of the neuronal function device of a metal oxide semiconductor-gated PNPN diode was evaluated. The variability of neurons is known to affect the inference accuracy of spiking neural networks (SNNs). The device has stochastic operation on its own, and the spike frequency can be controlled by the gate voltage, which has the possibility to improve the accuracy of SNNs.
