First Demonstration of Hysteresis-Free IGZO/SnO-Based Complementary Circuits and SRAM with Long-Term Reliability...

Area of Science:

• Materials Science
• Electrical Engineering
• Semiconductor Physics

Background:

• Development of advanced semiconductor materials is crucial for next-generation electronics.
• Oxide semiconductors offer unique properties for low-power applications.
• Complementary circuits require both n-type and p-type transistors for efficient operation.

Purpose of the Study:

• To demonstrate complementary logic and memory circuits using n-type Indium Gallium Zinc Oxide (IGZO) and p-type Tin Monoxide (SnO) thin-film transistors (TFTs).
• To achieve low-voltage, hysteresis-free operation with enhanced stability and reduced device variation.
• To explore the potential of these circuits for low-power system integration and future electronic applications.

Main Methods:

• Fabrication of n-type IGZO and p-type SnO TFTs.
• Optimization of IGZO channel thickness to tune inverter switching points.
• Application of SU-8 passivation to suppress bias-stress-induced degradation.
• Characterization of device performance, including voltage gain, power consumption, and stability.
• Demonstration of a 3-stage ring oscillator and 6T-Static Random-Access Memory (SRAM) cells.

Main Results:

• Achieved low-voltage, hysteresis-free operation with high voltage gain (146.6 V/V) and ultra-low static power consumption (nanowatt range).
• SU-8 passivation effectively improved long-term stability and reduced device variation in both IGZO and SnO TFTs.
• Demonstrated stable oscillations in a 3-stage ring oscillator and tunable static noise margins in 6T-SRAM cells.

Conclusions:

• IGZO/SnO complementary circuits enable high-performance, low-power logic and memory functions.
• The demonstrated devices exhibit excellent stability and reliability, suitable for practical applications.
• This work highlights the significant potential of oxide semiconductor complementary circuits for future low-power electronics, non-volatile memory, and on-device AI hardware.