A new advance in a two-century pursuit in physics

A new advance in a two-century pursuit in physics

Semiconductor devices are ubiquitous in modern electronics, powering everything from smartphones to computers. Accurately characterizing the electronic properties of these materials is critical for continued innovation, yet remains a complex challenge. IBM Research has demonstrated a breakthrough in semiconductor characterization, building upon nearly two centuries of progress and utilizing a principle from ancient mathematics.

Traditional methods, like Ohm’s Law and the Hall effect, provide fundamental insights but are limited in scope. Achieving a comprehensive understanding of semiconductor behavior requires simultaneous measurement of multiple parameters, a task previously hindered by experimental complexity and data interpretation. The new technique promises to streamline this process, offering a more complete picture of material properties.

Why This Matters

Current semiconductor characterization often relies on sequential measurements, each providing limited information. This iterative approach is time-consuming and can introduce inaccuracies. The cost of mischaracterizing materials can be substantial, leading to device failures and delays in product development – potentially impacting a multi-billion dollar industry.

Key Insights

• Ohm’s Law formulated in 1827: The foundation of conductivity measurement.
• Hall Effect, 1879: Enables determination of carrier type, mobility, and density.
• Carrier-Resolved Photo-Hall (CRPH) effect, 2019: Simultaneous measurement of electron and hole properties.
• Conic Sections (300 BCE): Ancient mathematical shapes now used to analyze semiconductor data.

Working Example

# Simplified example of calculating photo-Hall conductivity (conceptual)
# Note: Actual implementation involves complex physics and experimental setup

def calculate_photo_hall_conductivity(voltage, current, magnetic_field, light_intensity):
  """
  Calculates photo-Hall conductivity based on measured parameters.
  This is a simplified illustration and does not represent the full CRPH model.
  """
  hall_voltage = current * magnetic_field / voltage
  photo_hall_conductivity = hall_voltage / light_intensity
  return photo_hall_conductivity

# Example usage
voltage = 1.0  # Volts
current = 0.1  # Amps
magnetic_field = 0.5  # Tesla
light_intensity = 100.0 # W/m^2

photo_hall_conductivity = calculate_photo_hall_conductivity(voltage, current, magnetic_field, light_intensity)
print(f"Photo-Hall Conductivity: {photo_hall_conductivity} S/m")

Practical Applications

• Next-generation logic: IBM is using the technique to investigate new semiconductor materials beyond silicon for advanced computing.
• Defect Engineering: Identifying and controlling trap states in semiconductors to improve device performance and reliability.

References:

• https://research.ibm.com/blog/a-new-advance-in-a-two-century-pursuit-in-physics?utm_medium=rss&utm_source=rss
• https://doi.org/10.1103/PhysRev.129.168
• https://doi.org/10.1038/s41586-019-1698-z
• https://doi.org/10.1126/sciadv.adz0460