Phase-change random access memory (PCRAM) is a type of non-volatile memory based on chalcogenide glass. The resistance of PCRAM transitions between amorphous (high resistance) and crystalline (low resistance) states by controlled Joule heating and quenching. The resistance state of the memory is largely related to the size of the amorphous region and its controllability and stability. This makes PCRAM cells uniquely capable for storing multiple states (resistance) thus having the potential for higher effective cell density than conventional binary memories. PCRAM can support array configurations including one transistor with one memory (1T1R) array and the denser one selector with one memory (1S1R) array. TSMC has been exploring PCRAM materials, cell structures, and dedicated circuit designs to enable near-memory and in-memory computing for AI and ML.

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  • A 40nm Low-Power Logic Compatible Phase Change Memory Technology

    An embedded phase change memory technology in 40nm low-power logic platform is demonstrated with minimal added process complexity - two non-critical additional masks over standard logic. Specially designed hard mask and etching process was used to achieve 50% shrinkage of the memory cell bottom electrode dimension with same lithography tooling as the 40nm logic platform. Bottom electrode CD shrinkage along with optimization of the electrode materials in terms of electrical and thermal conductivity enabled significant (~4x) write current reduction attaining competitive levels of ~300 A at 40nm BE CD. Embedded PCM cells reported in this work demonstrated over 100x memory window - (RESET/SET resistance switching ratio), over 200k cycling endurance with extrapolated 10 year retention at 120 . In this work not only large switching resistance ratios but also highly-controllable resistance values that are almost independent of the PCM starting resistance state are presented along with the corresponding programing pulse requirements. The switching resistance ratio and resistance value controllability are key features for neural network and compute-in-memory applications. In this work, their benefits on design margins for energy efficient high-density binary neural network for inference applications aiming accuracy levels of well over 90% is asserted over an MNIST dataset.
  • A Logic-Compatible Low Power 1T1R PCRAM with Spacer Assisted Process and Multilevel Storage Capabilities

  • Observation of PCRAM Endurance Cycling Induced Porous GST Material

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