Materials and Devices for Neural Systems and Interfaces

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Presentation transcript:

Materials and Devices for Neural Systems and Interfaces Sumedha Gandharava, Sepideh Rastegar, Catherine Walker, Justin Stadlbauer, Kurtis D. Cantley Department of Electrical and Computer Engineering, Boise State University Thin-Film Transistors (TFTs) Neural Interfaces Culturing SH-SY5Y human neuroblastoma cells on graphene-based micro electrode arrays (MEAs) Electrical characteristics of cells are accessed via patch clamp electrophysiology Overview Adaptable artificial neural networks with learning and pattern recognition Digital logic on flexible substrates Advanced electrical testing Wafer probing Custom high-performance neural network characterization Thin-film transistors for analog, digital, and memory Memristors Microelectrode arrays PECVD Nano-crystalline silicon (nc-Si) High-k metal oxides Metal nanoparticles Flexible polymers Materials Devices Circuits Testing Devices utilizing PECVD nano-crystalline silicon deposited at 250 °C or less Ambipolar operation for complementary circuits Fig. 4 Neuron Circuit fabricated using optical lithography (a) (b) (c) [111] peak [200] peak [311] peak Fig. 7 a) Illustration of neuron cells on active microelectrode sensor arrays. b) Contact pads using Au for graphene arrays. c) Illustration of hexagonal planar graphene structure. Fig. 3 Cross-section and 3-D view of a TFT(Top left and right). Drain current versus gate voltage of a TFT(Bottom left). Input current, voltage of the input capacitor, output voltage and spike frequency of a TFT(Bottom right). TFT Memories Fig. 1 Plasma Enhanced Chemical Vapor Deposited (PECVD) nc-Si X-ray diffraction f Fig. 8 Probe station constructed (Left), electrophysiology experiment with reservoir-mounted graphene MEA using SH-SY5Y (human neuroblastoma line) cell (Right and below) Use nanoparticles to trap charge over millisecond time scales Used for artificial neural circuits with biologically realistic synaptic learning Resistive Memory (a) (b) Two-terminal devices that change resistance based on past current flow (Memristors) (c) (a) Memristors provide long-term storage of synaptic weights Fig. 5 a) AFM of CVD graphene. b) AFM of Au nanoparticles on graphene and on SiO2 Support and Funding Nanoparticle memory TFT controls short-term learning (b) Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP), grant FA9550-14-1-0188. Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grants #P20GM103408 and P20GM109095. Boise State Department of Electrical and Computer Engineering. (d) Flexible Electronics (e) Integration of specialized materials and devices onto mechanically flexible substrates Piezoelectric sensors from PVDF-TrFE copolymer Fig. 2 Memristor cross-sections (a) Metal-Metal contact (b) HfTiO active Layer (c) Metal-Semiconductor contact a-Si based Memristors (d) Finalized die (e) Vias on die using photolithography method. (f) I-V characteristic of a 2umx2um device (g) I-V characteristic of a 50umx50um device Fig. 6 a), b), and c) Polymer substrates such as PET, PEN, or polyimide can be bonded to a rigid Si carrier wafer using silicon (PDMS) for simplified processing (f) (g)