研究目的
To improve the performance of CMOS terahertz detectors by reducing parasitic capacitance and applying a drain current.
研究成果
The study demonstrates that reducing gate-source parasitic capacitance through non-self-aligned structures can improve RV by up to 155%. Applying a drain current further increases RV without changing NEP, providing a method for performance enhancement in CMOS THz detectors. The findings align with analytical models and offer insights for optimizing detector design in standard CMOS processes.
研究不足
The improvement in RV with applied current is accompanied by increased noise, requiring a trade-off between high RV and low NEP. The study is limited to specific CMOS processes and antenna designs (650GHz), and may not generalize to other frequencies or technologies.
1：Experimental Design and Method Selection:
The study uses a self-mixing CMOS THz detector with a MOSFET and integrated antenna. It employs parasitic capacitance reduction via non-self-aligned MOSFET structures and applies DC drain current to enhance performance. Analytical models based on small-signal equivalent circuits are used to predict improvements in voltage responsivity (RV) and noise equivalent power (NEP).
2：Sample Selection and Data Sources:
CMOS detectors fabricated with standard CMOS processes, featuring a 650GHz antenna. Samples include structures with varying source displacements (WS) and applied drain currents (Ids).
3：List of Experimental Equipment and Materials:
CMOS detectors, spectrum analyzer for noise measurement, equipment for applying gate bias (VG) and drain current (Ids).
4：Experimental Procedures and Operational Workflow:
Fabricate detectors with non-self-aligned structures by shifting source blocks to reduce gate-source overlap. Measure RV and NEP under different gate biases and drain currents. Use spectrum analyzer to measure noise at various frequencies and current levels.
5：Data Analysis Methods:
Analyze RV and NEP dependencies on WS and Ids. Fit noise data to linear equations (e.g., N ∝ f^{-α}) and compare with analytical models.
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