About

Semiconductors are the foundation of all modern technology, from computers to smartphones. They are special materials that can control the flow of electricity, acting sometimes like a conductor and sometimes like an insulator. By carefully designing and arranging semiconductor materials at the atomic level, scientists can create devices, like memristors, with unique properties that go far beyond traditional electronics.

The history of semiconductors dates back to the 19th century, with the invention of the rectifier in 1874 and the discovery of the semiconductor effect by Michael Faraday in 1833. In the early 20th century, the cat's-whisker detector was a primitive semiconductor diode used in radios.​

The field advanced rapidly after the invention of the transistor at Bell Laboratories in 1947 by John Bardeen and Walter Brattain, and further developed as the junction transistor by William Shockley in 1948. This innovation replaced bulky vacuum tubes and enabled devices to become faster, more reliable, and vastly smaller. The 1950s and 1960s saw the rise of the integrated circuit (IC), with Jack Kilby and Robert Noyce developing technology that allowed multiple semiconductor devices to be fabricated on a single chip—transforming computing and consumer electronics.​

Key milestones such as the invention of the MOSFET (metal-oxide-semiconductor field-effect transistor) in 1960 and the dominance of silicon as the preferred semiconductor material powered exponential growth, reflected in Moore’s law, which predicts a doubling of chip density approximately every two years. Advances in nanotechnology, such as the discovery of carbon nanotubes in 1991, point to future generations of semiconductor devices far beyond silicon’s limits. 

Neuromorphic computing is a new approach to building computers that work more like the human brain. Instead of processing information step by step like today’s computers, neuromorphic systems use networks of devices, such as memristors, to learn and adapt. This could lead to computers that are faster, more energy-efficient, and capable of tasks like pattern recognition, decision-making, and opening the door to new advances in artificial intelligence.

Neuromorphic computing draws inspiration not only from neuroscience, but also from advances in microelectronics and material science, such as memristors and advanced semiconductor technologies. This will enable devices to react instantly to data, adapt to new situations, and operate efficiently in complex, dynamic environments—making neuromorphic computing ideal for fields like autonomous vehicles, robotics, smart sensors, and healthcare diagnostics. By processing data locally and learning from experience, neuromorphic devices deliver faster responses and lower power consumption than traditional architectures, opening new possibilities for energy-efficient artificial intelligence.