Hardware implementation of metric algorithms for a self-mixing laser interferometric sensor

Self-mixing interferometry (SMI) has been widely used for sensing of diverse vibration, velocity, displacement, biomedical and flow applications. The simplicity of the SMI configuration enables the design of a low-cost, self-aligned and compact sensor with a small optical component count. SMI occurs when a small portion of emitted coherent optical beam is backscattered by the remote target and re-enters the laser cavity, causing interference. Under SMI, the employed laser diode simultaneously acts as a laser source, coherent detector, as well as micro-interferometer. This simple sensor design configuration, however, comes at a price of complex signal processing algorithms, because the SMI signals have rich characteristics as a function of optical feedback level. The purpose of this research work is to implement two of these algorithms, time-frequency signal processing (TFSP) and Consecutive samples based Unwrapping (CSU) in hardware so that real-time displacement and vibration measurements with nanometric precision can be retrieved from the SM sensor in an embedded, autonomous manner. We implemented and tested both algorithms on a Xilinx ZYBO Zynq 7000 development board using VHDL. Results show that our design of CSU algorithm is capable of operating at 432 MHZ minimum clock frequency and latency of 3 clock cycles. Our CSU design consumes 159 slice registers and 215 LUT's, and draws 0.70 watts of onboard power. While the design of TFSP algorithm is capable of operating at 363.3 MHZ clock frequency and latency of 570k clock cycles. TFSP design consumes 5056 slice registers and 5192 LUT's, and draws 0.8 watts of onboard power.