mpw-005 / URPM Ultra-high resistor poly mask

the URPM shapes of all of mpw-005

40 of 40 dies rendered.

119 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (4.4 MB in total, largest 110.9 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_URPM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_URPM_reticle.png
the stitched reticle, full resolution
3.5 MB de4ffb634659c725a8507e9974d85fc856218d3e5cc852c974518d03162a8cb01da20fa5 80e093521efe7c25fed76405a3484d9849743626f400cec618b179dcc1374695
mpw-005_URPM_reticle_overview.png
the same image scaled to fit 4096 px
14.8 kB fa6ee35a0f2ccccb25ffa5aef57b1b071fc32d3d2d1506e67731e2d6aa1d580d05e6bf41 8d06d18132c15028787260bfd649528eee642d9960a5d61ccddc8343d8ae5b7d
mpw-005_URPM_tiles.json
where every tile belongs, and its checksum
13.5 kB 67096c9fe6c5ba63e4569b48ac71135b4017c726cb655ff10b2ba8b62ffceccf5d01bd3b d6a386a890a2e19d9383fc68f93f0b7b4239d17663e17ca23d9df6317e4a0761
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
A2 mpw5_cache 7576×107767576, 0 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
B2 microwatt_mpw5 7576×107767576, 10776 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
B3 armleo_gpio_mpw5 7576×1077615152, 10776 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
B4 fabulous_efpga 7576×1077622728, 10776 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
B5 i2c_controller 7576×1077630304, 10776 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
B6 lbist-mbist 7576×1077637880, 10776 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 106.9 kB24fc4f09b4bd28cada855bff8a84875c9d42464781d49f18223fedf9c060cb09
C1 riscduino-qcore__q0_ 7576×107760, 21552 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
C2 high_speed_vco 7576×107767576, 21552 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
C3 openfasoc-cryo-gen 7576×1077615152, 21552 103.1 kB27aed7d548f900053c9fd09a02247d519543a45b7ebba0b9c8ebb36d52c43b9f
C4 microwave_signal_generator 7576×1077622728, 21552 103.4 kB02ae4f51cd5212f25b1750772f256891f7d955994038956ff5e9dc5cab02082e
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 103.5 kB148c7dec4844986c076f37518af72a110daaf7ff844c811e4ed771a9990d4aca
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 107.1 kBdfa7d26e91813355e355e82e20de77912854ea1e544c03b6ef1cb9e530c25992
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 103.2 kBbe611b8676bc264826b2cd1907fc856c50d715312bb74ca3b09f5ed93f71037f
D2 azadi_iii 7576×107767576, 32328 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
D3 tia_for_physic_experiments 7576×1077615152, 32328 103.2 kB95cf6b9c0aaf1ea7efc670e32e88956f3838ae09e3696c2afdbf68c6fbcbb3f5
D4 qf105 7576×1077622728, 32328 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
D6 junga_soc_mpw5 7576×1077637880, 32328 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
D7 nand_flash_mpw-5 7976×1077645456, 32328 106.9 kB24fc4f09b4bd28cada855bff8a84875c9d42464781d49f18223fedf9c060cb09
E1 kasirga-c0-mpw-5 7576×107760, 43104 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
E3 wirelessenergyharvesting 7576×1077615152, 43104 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
E4 caravel_raster_engine 7576×1077622728, 43104 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 102.9 kBec7014608b651572d2bb9f21d4f0969ce994d0f8f3e0c0a2b1a9c4e2f4cee026
E7 alu 7976×1077645456, 43104 106.9 kB24fc4f09b4bd28cada855bff8a84875c9d42464781d49f18223fedf9c060cb09
F1 4ft4 7576×111760, 53880 106.7 kB3519556765611d90ce00475d414ac90855795d869e456031d86492340d3de707
F2 rng_chaos 7576×111767576, 53880 106.7 kB3519556765611d90ce00475d414ac90855795d869e456031d86492340d3de707
F3 uetrv-ecore 7576×1117615152, 53880 106.7 kB3519556765611d90ce00475d414ac90855795d869e456031d86492340d3de707
F4 systolic_array 7576×1117622728, 53880 106.7 kB3519556765611d90ce00475d414ac90855795d869e456031d86492340d3de707
F5 reram_test 7576×1117630304, 53880 106.9 kBc6f5c7e3b17e8d68718624f4b7989fdbb2fee20259fbea6978e562ce0803e122
F6 secure_memory 7576×1117637880, 53880 106.7 kB3519556765611d90ce00475d414ac90855795d869e456031d86492340d3de707
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 110.9 kBf4003129e59f6711282296e4e6ff16c7a66ed7d6da5e53cd32fc7a676a5b7686

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About URPM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask URPM

Mask layer
URPM — Ultra-high resistor poly mask
Process step
55 — Ultra-high resistor poly mask
Drawn layer
79:20

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.