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Dr. Semiconductor turns his backyard shed into a functional cleanroom, crafting working DRAM from scratch and offering a DIY solution to the global memory shortage driven by AI demands.
If you're a tech enthusiast or developer keeping an eye on hardware prices, you’ve likely noticed the surge in RAM costs driven by AI's insatiable demand for memory. This has led to higher prices and shortages, making it challenging to get your hands on affordable DRAM (Dynamic Random Access Memory). But what if you could build your own?
Enter Dr. Semiconductor, a YouTuber who recently demonstrated how to create working DRAM in his backyard shed turned cleanroom. His project is not just a fascinating DIY endeavor but also a practical response to the memory crisis.
Dr. Semiconductor's journey began with converting a small shed into a makeshift cleanroom. This setup, while far from the industrial standards of Samsung or Micron, was sufficient for his experimental purposes. In his first video, he detailed the process of transforming the space, ensuring it met the necessary cleanliness levels for semiconductor fabrication.
In his second video, Dr. Semiconductor walks through the intricate steps of creating a 5x4 array of capacitors and transistors. This is a tiny fraction of the number of cells on a modern DRAM chip but serves as an excellent proof of concept. Here’s a breakdown of the process:

After fabricating a few sample chips, Dr. Semiconductor tested them using parameter analyzers. Given the small size of the 5x4 array, he needed to use micromanipulators and extremely fine probes to measure the device accurately. The tests confirmed that the DIY DRAM cells were functional, marking a significant milestone in his project.
While building your own DRAM at home is not a practical solution for large-scale production, Dr. Semiconductor’s project highlights several important points:
Dr. Semiconductor’s project is a testament to the ingenuity and perseverance of hobbyists in the face of technological challenges. While it may not solve the memory crisis on its own, it offers valuable insights into the semiconductor manufacturing process and serves as an inspiration for those looking to push the boundaries of what can be achieved with limited resources.
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Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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25 April 2026
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