Neuromorphic computing has recently emerged as one of the most promising option to reduce power consumption of big data analysis, paving the way for artificial intelligence systems with power efficiencies like the human brain. The key device for neuromorphic computing system is given by artificial two-terminal synapses controlling signal processing and transmission. Their conductivity must be changed in an analog/continuous way depending on neural signal strengths. In addition, synaptic devices must have: symmetric/linear conductivity potentiation and depression; a high number of levels (~32), which depend on applications and algorithm performances; high data retention (>10 years) and cycling (>109); ultra-low power consumption (<10fJ); low variability; high scalability (<10nm) and possibility of 3D integration.
A variety of different device technologies have been explored such as phase change memories, ferroelectric random-access memory and resistive random-access memory (RRAM). In each case matching the desired specs is a complex multivariable problem requiring a deep quantitative understanding of the link between material properties at the atomic scale and electrical device performance. We have used a multiscale modeling platform GINESTRATM to illustrate this for the case of RRAM and Ferroelectric tunnel junctions (FTJ).
In the case of RRAM, modeling of key mechanisms shows that a dielectric stack composed of two appropriately chosen dielectrics provides the best solution, in agreement with experimental data. In the case of FTJ, the hysteretic ferroelectric behavior of dielectric stacks fabricated from the orthorhombic phase of doped HfO2 is nicely captured by the simulations. These show that Fe-HfO2 stack can be easily used for analog switching by simply tuning set/reset voltage amplitudes. An added advantage of the simulations is that they point out ways to improve the performance, variability and endurance of the devices in order to meet industrial requirements.
Dipu Pramanik is the currently the CEO/Co-founder of MDLSoft Inc. which provides a multiscale modeling platform linking materials to devices to enable the rapid development of future electronic devices. He received his PhD in Physics/Materials Science from Cornell and over the past 35 years has been intimately involved with technologies driving the growth of the semiconductor industry. Prior to MDLSoft he was VP/Fellow at Intermolecular Inc, where he created the workflow for developing novel devices from new and existing materials. He ran the TCAD product line at Synopsys and grew it into the dominant player in the field before moving to Cadence as VP of the DFM business unit. For more than 20 years, Dr Pramanik, worked at various semiconductor companies developing successive generations of Process and Design Technology. In these roles he covered every aspect of the product rollout from process, design, packaging, test to manufacturing and reliability.